Neighborhood Clustering in Toronto
In completion of requirements for the IBM Data Science Professional Certificate on Coursera
Daniel Nezich
This project uses location and census data to group neighborhoods in Toronto, CA in order to assist, for example, in moving across town to a similar neighborhood or determining which area of the city might be most enjoyable to visit.
First, the locations to be grouped are defined as the Forward Sortation Area (FSA) regions of Canada Post. A list of FSA and corresponding neighborhoods in Totronto is read from Wikipedia. The geolocation service HERE is used to find a latitude and longitude for each FSA.
Second, features are generated for each location. These come in two forms: numerical data from the Canada 2016 Census, and a list of nearby venues from Foursquare. The numerical features are modified to approximate a normal distribution, and the list of venues is converted into categorical vectors. Various levels of categorization are prepared for exploration.
Third, a k-means model is used to fit the features. A range of feature sets is used to see how clustering performance is affected by feature choice, and for each feature set a range of cluster number is examined to determine the optimal cluster number via the gap statistic.
Fourth, an optimal model is selected and examined in detail, including determination of important predictive features by ANOVA p-value, cluster identification by average feature value, and cluster display on a map.
# General
import pandas as pd
import numpy as np
# Postal Codes
import requests
# from bs4 import BeautifulSoup # had to install to environment in Anaconda
import lxml # had to install to environment in Anaconda, backdated to 4.6.1 (4.6.2 current) for pandas read_html()
import html5lib # had to install to environment in Anaconda (1.1 current) for pandas read_html()
# Geocoding
import config
from geopy.geocoders import Here
from geopy import distance
import matplotlib.pyplot as plt
%matplotlib inline
# Census Features
import folium
import matplotlib.pyplot as plt
%matplotlib inline
# Venue Features
import os
import dill
import config
import importlib
import requests
import matplotlib.pyplot as plt
%matplotlib inline
# K-Means Clustering
import sklearn.feature_selection
from sklearn.cluster import KMeans
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# Optimal K-Means Model
import folium
import matplotlib.cm as cm
import matplotlib.colors
from folium.features import DivIcon
First we will use the Requests module to get the webpage containing the data we need.
import requests
url_wikipedia_postal_codes = 'https://web.archive.org/web/20201112041516/https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M' #'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'
webpage = requests.get(url_wikipedia_postal_codes)
We could inspect webpage.text and extract the tables with BeautifulSoup (see appendix), but parsing tables from the webpage text can also be handled by Pandas.
Let's inspect the tables automatically parsed from the page:
df = pd.read_html(webpage.text)
for i, d in enumerate(df):
print(f'Table {i}:')
display(d.head())
Table 0:
| Postal Code | Borough | Neighbourhood | |
|---|---|---|---|
| 0 | M1A | Not assigned | Not assigned |
| 1 | M2A | Not assigned | Not assigned |
| 2 | M3A | North York | Parkwoods |
| 3 | M4A | North York | Victoria Village |
| 4 | M5A | Downtown Toronto | Regent Park, Harbourfront |
Table 1:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Canadian postal codes | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | NL NS PE NB QC ON MB SK AB BC NU/NT YT A B C E... | NL NS PE NB QC ON MB SK AB BC NU/NT YT A B C E... | NL NS PE NB QC ON MB SK AB BC NU/NT YT A B C E... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | NL | NS | PE | NB | QC | QC | QC | ON | ON | ON | ON | ON | MB | SK | AB | BC | NU/NT | YT |
| 3 | A | B | C | E | G | H | J | K | L | M | N | P | R | S | T | V | X | Y |
Table 2:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NL | NS | PE | NB | QC | QC | QC | ON | ON | ON | ON | ON | MB | SK | AB | BC | NU/NT | YT |
| 1 | A | B | C | E | G | H | J | K | L | M | N | P | R | S | T | V | X | Y |
By inspection, the table we want is at index 0 (current Wikipedia formatting has been updated to break this parsing, but the Internet Archive has the prior version, so the analysis is retained and the link above updated).
df = df[0].astype(str)
df.shape
(180, 3)
Let's clean up the columns.
First, the Postal Code is expected to be unique:
len(df['Postal Code'].unique()) == len(df['Postal Code'])
True
So rows are uniquely indexed by the Postal Code, as desired, and we do not have to combine neighborhoods into a comma separated list as per the assignment instructions - they already are.
Second, we take only the Borough that are not 'Not Assigned':
df = df[df['Borough']!='Not assigned'].reset_index(drop=True)
display(df.head())
df.shape
| Postal Code | Borough | Neighbourhood | |
|---|---|---|---|
| 0 | M3A | North York | Parkwoods |
| 1 | M4A | North York | Victoria Village |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights |
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government |
(103, 3)
So 77 Postal Codes were not assigned to a Borough.
We should also ensure that all Borough names are legitimate. This must be done manually.
df['Borough'].unique()
array(['North York', 'Downtown Toronto', 'Etobicoke', 'Scarborough',
'East York', 'York', 'East Toronto', 'West Toronto',
'Central Toronto', 'Mississauga'], dtype=object)
Those all look like Borough names, and there are 10 total.
Third, we want each entry in Neighborhood that is 'Not assigned' to be the Borough name.
def showalldf(df):
old_opt = pd.get_option('display.max_rows')
numel = len(df.index)
pd.set_option('display.max_rows', len(df.index))
display(df)
pd.set_option('display.max_rows', old_opt)
print(f"There are {df[df['Neighbourhood']=='Not assigned'].size} Neighborhoods not assigned")
print('Neighborhoods are:')
showalldf(df['Neighbourhood'])
There are 0 Neighborhoods not assigned Neighborhoods are:
0 Parkwoods 1 Victoria Village 2 Regent Park, Harbourfront 3 Lawrence Manor, Lawrence Heights 4 Queen's Park, Ontario Provincial Government 5 Islington Avenue, Humber Valley Village 6 Malvern, Rouge 7 Don Mills 8 Parkview Hill, Woodbine Gardens 9 Garden District, Ryerson 10 Glencairn 11 West Deane Park, Princess Gardens, Martin Grov... 12 Rouge Hill, Port Union, Highland Creek 13 Don Mills 14 Woodbine Heights 15 St. James Town 16 Humewood-Cedarvale 17 Eringate, Bloordale Gardens, Old Burnhamthorpe... 18 Guildwood, Morningside, West Hill 19 The Beaches 20 Berczy Park 21 Caledonia-Fairbanks 22 Woburn 23 Leaside 24 Central Bay Street 25 Christie 26 Cedarbrae 27 Hillcrest Village 28 Bathurst Manor, Wilson Heights, Downsview North 29 Thorncliffe Park 30 Richmond, Adelaide, King 31 Dufferin, Dovercourt Village 32 Scarborough Village 33 Fairview, Henry Farm, Oriole 34 Northwood Park, York University 35 East Toronto, Broadview North (Old East York) 36 Harbourfront East, Union Station, Toronto Islands 37 Little Portugal, Trinity 38 Kennedy Park, Ionview, East Birchmount Park 39 Bayview Village 40 Downsview 41 The Danforth West, Riverdale 42 Toronto Dominion Centre, Design Exchange 43 Brockton, Parkdale Village, Exhibition Place 44 Golden Mile, Clairlea, Oakridge 45 York Mills, Silver Hills 46 Downsview 47 India Bazaar, The Beaches West 48 Commerce Court, Victoria Hotel 49 North Park, Maple Leaf Park, Upwood Park 50 Humber Summit 51 Cliffside, Cliffcrest, Scarborough Village West 52 Willowdale, Newtonbrook 53 Downsview 54 Studio District 55 Bedford Park, Lawrence Manor East 56 Del Ray, Mount Dennis, Keelsdale and Silverthorn 57 Humberlea, Emery 58 Birch Cliff, Cliffside West 59 Willowdale, Willowdale East 60 Downsview 61 Lawrence Park 62 Roselawn 63 Runnymede, The Junction North 64 Weston 65 Dorset Park, Wexford Heights, Scarborough Town... 66 York Mills West 67 Davisville North 68 Forest Hill North & West, Forest Hill Road Park 69 High Park, The Junction South 70 Westmount 71 Wexford, Maryvale 72 Willowdale, Willowdale West 73 North Toronto West, Lawrence Park 74 The Annex, North Midtown, Yorkville 75 Parkdale, Roncesvalles 76 Canada Post Gateway Processing Centre 77 Kingsview Village, St. Phillips, Martin Grove ... 78 Agincourt 79 Davisville 80 University of Toronto, Harbord 81 Runnymede, Swansea 82 Clarks Corners, Tam O'Shanter, Sullivan 83 Moore Park, Summerhill East 84 Kensington Market, Chinatown, Grange Park 85 Milliken, Agincourt North, Steeles East, L'Amo... 86 Summerhill West, Rathnelly, South Hill, Forest... 87 CN Tower, King and Spadina, Railway Lands, Har... 88 New Toronto, Mimico South, Humber Bay Shores 89 South Steeles, Silverstone, Humbergate, Jamest... 90 Steeles West, L'Amoreaux West 91 Rosedale 92 Stn A PO Boxes 93 Alderwood, Long Branch 94 Northwest, West Humber - Clairville 95 Upper Rouge 96 St. James Town, Cabbagetown 97 First Canadian Place, Underground city 98 The Kingsway, Montgomery Road, Old Mill North 99 Church and Wellesley 100 Business reply mail Processing Centre, South C... 101 Old Mill South, King's Mill Park, Sunnylea, Hu... 102 Mimico NW, The Queensway West, South of Bloor,... Name: Neighbourhood, dtype: object
So all Neighbourhood entries look non-empty.
But on closer inspection there are entries that don't make sense for a neighborhood analysis, so we should drop them:
These will actually be removed in a later step where we eliminate postal codes that have no residential space associated with them (like govenment buildings, office buildings, commercial plazas). We will keep the full dataframe for now. The drop on inspection was previously (and incompletely) performed with the line:
df_cleaned = df.drop(index=[76, 92, 100]).reset_index(drop=True)
We conclude this section by displaying the final dataframe:
showalldf(df)
| Postal Code | Borough | Neighbourhood | |
|---|---|---|---|
| 0 | M3A | North York | Parkwoods |
| 1 | M4A | North York | Victoria Village |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights |
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government |
| 5 | M9A | Etobicoke | Islington Avenue, Humber Valley Village |
| 6 | M1B | Scarborough | Malvern, Rouge |
| 7 | M3B | North York | Don Mills |
| 8 | M4B | East York | Parkview Hill, Woodbine Gardens |
| 9 | M5B | Downtown Toronto | Garden District, Ryerson |
| 10 | M6B | North York | Glencairn |
| 11 | M9B | Etobicoke | West Deane Park, Princess Gardens, Martin Grov... |
| 12 | M1C | Scarborough | Rouge Hill, Port Union, Highland Creek |
| 13 | M3C | North York | Don Mills |
| 14 | M4C | East York | Woodbine Heights |
| 15 | M5C | Downtown Toronto | St. James Town |
| 16 | M6C | York | Humewood-Cedarvale |
| 17 | M9C | Etobicoke | Eringate, Bloordale Gardens, Old Burnhamthorpe... |
| 18 | M1E | Scarborough | Guildwood, Morningside, West Hill |
| 19 | M4E | East Toronto | The Beaches |
| 20 | M5E | Downtown Toronto | Berczy Park |
| 21 | M6E | York | Caledonia-Fairbanks |
| 22 | M1G | Scarborough | Woburn |
| 23 | M4G | East York | Leaside |
| 24 | M5G | Downtown Toronto | Central Bay Street |
| 25 | M6G | Downtown Toronto | Christie |
| 26 | M1H | Scarborough | Cedarbrae |
| 27 | M2H | North York | Hillcrest Village |
| 28 | M3H | North York | Bathurst Manor, Wilson Heights, Downsview North |
| 29 | M4H | East York | Thorncliffe Park |
| 30 | M5H | Downtown Toronto | Richmond, Adelaide, King |
| 31 | M6H | West Toronto | Dufferin, Dovercourt Village |
| 32 | M1J | Scarborough | Scarborough Village |
| 33 | M2J | North York | Fairview, Henry Farm, Oriole |
| 34 | M3J | North York | Northwood Park, York University |
| 35 | M4J | East York | East Toronto, Broadview North (Old East York) |
| 36 | M5J | Downtown Toronto | Harbourfront East, Union Station, Toronto Islands |
| 37 | M6J | West Toronto | Little Portugal, Trinity |
| 38 | M1K | Scarborough | Kennedy Park, Ionview, East Birchmount Park |
| 39 | M2K | North York | Bayview Village |
| 40 | M3K | North York | Downsview |
| 41 | M4K | East Toronto | The Danforth West, Riverdale |
| 42 | M5K | Downtown Toronto | Toronto Dominion Centre, Design Exchange |
| 43 | M6K | West Toronto | Brockton, Parkdale Village, Exhibition Place |
| 44 | M1L | Scarborough | Golden Mile, Clairlea, Oakridge |
| 45 | M2L | North York | York Mills, Silver Hills |
| 46 | M3L | North York | Downsview |
| 47 | M4L | East Toronto | India Bazaar, The Beaches West |
| 48 | M5L | Downtown Toronto | Commerce Court, Victoria Hotel |
| 49 | M6L | North York | North Park, Maple Leaf Park, Upwood Park |
| 50 | M9L | North York | Humber Summit |
| 51 | M1M | Scarborough | Cliffside, Cliffcrest, Scarborough Village West |
| 52 | M2M | North York | Willowdale, Newtonbrook |
| 53 | M3M | North York | Downsview |
| 54 | M4M | East Toronto | Studio District |
| 55 | M5M | North York | Bedford Park, Lawrence Manor East |
| 56 | M6M | York | Del Ray, Mount Dennis, Keelsdale and Silverthorn |
| 57 | M9M | North York | Humberlea, Emery |
| 58 | M1N | Scarborough | Birch Cliff, Cliffside West |
| 59 | M2N | North York | Willowdale, Willowdale East |
| 60 | M3N | North York | Downsview |
| 61 | M4N | Central Toronto | Lawrence Park |
| 62 | M5N | Central Toronto | Roselawn |
| 63 | M6N | York | Runnymede, The Junction North |
| 64 | M9N | York | Weston |
| 65 | M1P | Scarborough | Dorset Park, Wexford Heights, Scarborough Town... |
| 66 | M2P | North York | York Mills West |
| 67 | M4P | Central Toronto | Davisville North |
| 68 | M5P | Central Toronto | Forest Hill North & West, Forest Hill Road Park |
| 69 | M6P | West Toronto | High Park, The Junction South |
| 70 | M9P | Etobicoke | Westmount |
| 71 | M1R | Scarborough | Wexford, Maryvale |
| 72 | M2R | North York | Willowdale, Willowdale West |
| 73 | M4R | Central Toronto | North Toronto West, Lawrence Park |
| 74 | M5R | Central Toronto | The Annex, North Midtown, Yorkville |
| 75 | M6R | West Toronto | Parkdale, Roncesvalles |
| 76 | M7R | Mississauga | Canada Post Gateway Processing Centre |
| 77 | M9R | Etobicoke | Kingsview Village, St. Phillips, Martin Grove ... |
| 78 | M1S | Scarborough | Agincourt |
| 79 | M4S | Central Toronto | Davisville |
| 80 | M5S | Downtown Toronto | University of Toronto, Harbord |
| 81 | M6S | West Toronto | Runnymede, Swansea |
| 82 | M1T | Scarborough | Clarks Corners, Tam O'Shanter, Sullivan |
| 83 | M4T | Central Toronto | Moore Park, Summerhill East |
| 84 | M5T | Downtown Toronto | Kensington Market, Chinatown, Grange Park |
| 85 | M1V | Scarborough | Milliken, Agincourt North, Steeles East, L'Amo... |
| 86 | M4V | Central Toronto | Summerhill West, Rathnelly, South Hill, Forest... |
| 87 | M5V | Downtown Toronto | CN Tower, King and Spadina, Railway Lands, Har... |
| 88 | M8V | Etobicoke | New Toronto, Mimico South, Humber Bay Shores |
| 89 | M9V | Etobicoke | South Steeles, Silverstone, Humbergate, Jamest... |
| 90 | M1W | Scarborough | Steeles West, L'Amoreaux West |
| 91 | M4W | Downtown Toronto | Rosedale |
| 92 | M5W | Downtown Toronto | Stn A PO Boxes |
| 93 | M8W | Etobicoke | Alderwood, Long Branch |
| 94 | M9W | Etobicoke | Northwest, West Humber - Clairville |
| 95 | M1X | Scarborough | Upper Rouge |
| 96 | M4X | Downtown Toronto | St. James Town, Cabbagetown |
| 97 | M5X | Downtown Toronto | First Canadian Place, Underground city |
| 98 | M8X | Etobicoke | The Kingsway, Montgomery Road, Old Mill North |
| 99 | M4Y | Downtown Toronto | Church and Wellesley |
| 100 | M7Y | East Toronto | Business reply mail Processing Centre, South C... |
| 101 | M8Y | Etobicoke | Old Mill South, King's Mill Park, Sunnylea, Hu... |
| 102 | M8Z | Etobicoke | Mimico NW, The Queensway West, South of Bloor,... |
And finally, the postal codes dataframe shape is:
df.shape
(103, 3)
After looking through many geopy and geocoder options (see Appendix), I chose to use Nominatim. While Nominatim doesn't succeed for partial postal code lookup, it did succeed with many neighborhoods. Sevearal missed assignments were due to using slightly different names, but there were still over 80 imperfectly assigned neighborhood locations.
I then tried using a commercial service. Google appears to charge for any developer use, but HERE has a freemium service which I did sign up for. With an API key I was able to get geopy to geocode a partial postal code, so I'll be continuing work there. Note that the geocoder module does not seem to work, possibly due to a misconfigured web address for requests (based on the errors returned).
To keep api keys secret, they are stored in a config.py file listed in the .gitignore file on GitHub.
To reload the configuration file should it need to be changed in development, run:
import importlib
importlib.reload(config)
For HERE, we need supply just the REST API key (on their site, called the app code)
import config # Local python file storing API keys
from geopy.geocoders import Here
gc = Here(apikey=config.HERE_APIKEY)
Now we can try to geocode the postal codes
df_c = df.copy(deep=True)
for i, row in df_c.iterrows():
g = gc.geocode(f"{row['Postal Code']}, Ontario")
if g!=None:
df_c.loc[i,'GString'] = g[0]
df_c.loc[i,'Latitude'] = g[1][0]
df_c.loc[i,'Longitude'] = g[1][1]
else:
print(f"Geocoding failed for Postal Code: {row['Postal Code']}, Borough: {row['Borough']}, Neighborhoods: {row['Neighbourhood']}")
Geocoding failed for Postal Code: M7R, Borough: Mississauga, Neighborhoods: Canada Post Gateway Processing Centre Geocoding failed for Postal Code: M5W, Borough: Downtown Toronto, Neighborhoods: Stn A PO Boxes Geocoding failed for Postal Code: M7Y, Borough: East Toronto, Neighborhoods: Business reply mail Processing Centre, South Central Letter Processing Plant Toronto
Offline inspection for missing values:
print(f"There are {df_c[df_c['GString']==None].shape[0]} rows where geocoding failed")
df_tmp = df_c['GString'].copy()
for i, val in enumerate(df_tmp):
if pd.isnull(val):
df_tmp[i] = -1
else:
df_tmp[i] = val.find(df_c.loc[i,'Postal Code'])
print(f"There are {sum(df_tmp==-1)} rows where the postal code is not found in the geocoded Location")
if sum(df_tmp==-1)>0:
print(f"The geocoding failed at indices {df_tmp[df_tmp==-1].index.to_list()}, entries shown below:")
display(df_c.loc[df_tmp==-1,:])
There are 0 rows where geocoding failed There are 3 rows where the postal code is not found in the geocoded Location The geocoding failed at indices [76, 92, 100], entries shown below:
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | |
|---|---|---|---|---|---|---|
| 76 | M7R | Mississauga | Canada Post Gateway Processing Centre | NaN | NaN | NaN |
| 92 | M5W | Downtown Toronto | Stn A PO Boxes | NaN | NaN | NaN |
| 100 | M7Y | East Toronto | Business reply mail Processing Centre, South C... | NaN | NaN | NaN |
Wow, that was so much easier than trying to massage the Nominatim data (see Appendix).
Note that these three indices are the ones we previously considered for removal by manual inspection of the neighborhood list. We can remove them here.
df_c.drop(index=df_tmp[df_tmp==-1].index.to_list(), inplace=True)
df_c.reset_index(drop=True, inplace=True)
df_c.shape
(100, 6)
Let's load up a csv file of locations and see if it matches.
df_gt = pd.read_csv('Geospatial_Coordinates.csv')
df_gt.head()
| Postal Code | Latitude | Longitude | |
|---|---|---|---|
| 0 | M1B | 43.806686 | -79.194353 |
| 1 | M1C | 43.784535 | -79.160497 |
| 2 | M1E | 43.763573 | -79.188711 |
| 3 | M1G | 43.770992 | -79.216917 |
| 4 | M1H | 43.773136 | -79.239476 |
df_gt.shape
(103, 3)
df_gt.rename(columns={'Latitude':'GT Latitude','Longitude':'GT Longitude'}, inplace=True)
df_gt.head()
| Postal Code | GT Latitude | GT Longitude | |
|---|---|---|---|
| 0 | M1B | 43.806686 | -79.194353 |
| 1 | M1C | 43.784535 | -79.160497 |
| 2 | M1E | 43.763573 | -79.188711 |
| 3 | M1G | 43.770992 | -79.216917 |
| 4 | M1H | 43.773136 | -79.239476 |
df_d = df_c.merge(df_gt, on='Postal Code')
df_d
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | GT Latitude | GT Longitude | |
|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.32760 | 43.753259 | -79.329656 |
| 1 | M4A | North York | Victoria Village | M4A, Toronto, ON, Canada, Toronto, ON M4A, CAN | 43.73064 | -79.31297 | 43.725882 | -79.315572 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | M5A, Toronto, ON, Canada, Toronto, ON M5A, CAN | 43.65512 | -79.36264 | 43.654260 | -79.360636 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | M6A, Toronto, ON, Canada, Toronto, ON M6A, CAN | 43.72329 | -79.45099 | 43.718518 | -79.464763 |
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government | M7A, Toronto, ON, Canada, Toronto, ON M7A, CAN | 43.66189 | -79.39172 | 43.662301 | -79.389494 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 95 | M5X | Downtown Toronto | First Canadian Place, Underground city | M5X, Toronto, ON, Canada, Toronto, ON M5X, CAN | 43.64798 | -79.38144 | 43.648429 | -79.382280 |
| 96 | M8X | Etobicoke | The Kingsway, Montgomery Road, Old Mill North | M8X, Toronto, ON, Canada, Toronto, ON M8X, CAN | 43.65322 | -79.51114 | 43.653654 | -79.506944 |
| 97 | M4Y | Downtown Toronto | Church and Wellesley | M4Y, Toronto, ON, Canada, Toronto, ON M4Y, CAN | 43.66659 | -79.38133 | 43.665860 | -79.383160 |
| 98 | M8Y | Etobicoke | Old Mill South, King's Mill Park, Sunnylea, Hu... | M8Y, Toronto, ON, Canada, Toronto, ON M8Y, CAN | 43.63299 | -79.48968 | 43.636258 | -79.498509 |
| 99 | M8Z | Etobicoke | Mimico NW, The Queensway West, South of Bloor,... | M8Z, Toronto, ON, Canada, Toronto, ON M8Z, CAN | 43.62517 | -79.52703 | 43.628841 | -79.520999 |
100 rows × 8 columns
df_d['Diff Lat'] = df_d['Latitude']-df_d['GT Latitude']
df_d['Diff Lon'] = df_d['Longitude']-df_d['GT Longitude']
df_d.head()
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | GT Latitude | GT Longitude | Diff Lat | Diff Lon | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.32760 | 43.753259 | -79.329656 | -0.003279 | 0.002056 |
| 1 | M4A | North York | Victoria Village | M4A, Toronto, ON, Canada, Toronto, ON M4A, CAN | 43.73064 | -79.31297 | 43.725882 | -79.315572 | 0.004758 | 0.002602 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | M5A, Toronto, ON, Canada, Toronto, ON M5A, CAN | 43.65512 | -79.36264 | 43.654260 | -79.360636 | 0.000860 | -0.002004 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | M6A, Toronto, ON, Canada, Toronto, ON M6A, CAN | 43.72329 | -79.45099 | 43.718518 | -79.464763 | 0.004772 | 0.013773 |
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government | M7A, Toronto, ON, Canada, Toronto, ON M7A, CAN | 43.66189 | -79.39172 | 43.662301 | -79.389494 | -0.000411 | -0.002226 |
print(f"Latitude difference: {df_d['Diff Lat'].abs().mean():0.5f} +/- {df_d['Diff Lat'].abs().std():0.5f} with maximum {df_d['Diff Lat'].abs().max():0.5f}")
print(f"Longitude difference: {df_d['Diff Lon'].abs().mean():0.5f} +/- {df_d['Diff Lon'].abs().std():0.5f} with maximum {df_d['Diff Lon'].abs().max():0.5f}")
Latitude difference: 0.00261 +/- 0.00263 with maximum 0.01816 Longitude difference: 0.00356 +/- 0.00321 with maximum 0.01551
What do these differences mean in terms of distance?
from geopy import distance
for i, row in df_d.iterrows():
df_d.loc[i,'GT Diff Meters'] = distance.distance((row['Latitude'],row['Longitude']),(row['GT Latitude'],row['GT Longitude'])).meters
print(f"Differences: {df_d['GT Diff Meters'].mean():0.1f} +/- {df_d['GT Diff Meters'].std():0.1f} meters with maximum {df_d['GT Diff Meters'].max():0.1f} meters and minimum {df_d['GT Diff Meters'].min():0.1f} meters")
df_d.head()
Differences: 444.2 +/- 347.6 meters with maximum 2127.8 meters and minimum 13.4 meters
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | GT Latitude | GT Longitude | Diff Lat | Diff Lon | GT Diff Meters | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.32760 | 43.753259 | -79.329656 | -0.003279 | 0.002056 | 400.163722 |
| 1 | M4A | North York | Victoria Village | M4A, Toronto, ON, Canada, Toronto, ON M4A, CAN | 43.73064 | -79.31297 | 43.725882 | -79.315572 | 0.004758 | 0.002602 | 568.656678 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | M5A, Toronto, ON, Canada, Toronto, ON M5A, CAN | 43.65512 | -79.36264 | 43.654260 | -79.360636 | 0.000860 | -0.002004 | 187.801580 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | M6A, Toronto, ON, Canada, Toronto, ON M6A, CAN | 43.72329 | -79.45099 | 43.718518 | -79.464763 | 0.004772 | 0.013773 | 1230.009537 |
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government | M7A, Toronto, ON, Canada, Toronto, ON M7A, CAN | 43.66189 | -79.39172 | 43.662301 | -79.389494 | -0.000411 | -0.002226 | 185.294364 |
That's a very big difference! Though most are smaller, we'd still like some confidence in knowing which to use.
Manually inspecting Google Maps, the outline for M6A postal code has the HERE Lat/Long at the center, and the csv value near the left edge of the zone. For M8Y, both HERE and csv are a little off, though HERE is probably closer. Ideally with the postal code polygons we could find the center of mass, but that level of exactness won't help us.
Let's look at the distribution of nearest-neighbor distances, since this might inform our choice of neighborhood radius.
dist_matrix = []
for i, row in df_d.iterrows():
# This implementation is slow by a factor of 2, look into distance matrix functions
dist_matrix.append([distance.distance((row['Latitude'],row['Longitude']),(x['Latitude'],x['Longitude'])).meters for j, x in df_d.iterrows()])
df_d.loc[i,'NN Distance'] = min([x for x in dist_matrix[-1] if x > 0])
df_d.loc[i,'NN Index'] = np.argmin(np.array([(9999999 if x==0 else x) for x in dist_matrix[-1]]))
dist_nn = [min([x for x in y if x > 0]) for y in dist_matrix]
df_d['NN Index'] = df_d['NN Index'].astype(int)
df_d.head()
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | GT Latitude | GT Longitude | Diff Lat | Diff Lon | GT Diff Meters | NN Distance | NN Index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.32760 | 43.753259 | -79.329656 | -0.003279 | 0.002056 | 400.163722 | 2179.358574 | 71 |
| 1 | M4A | North York | Victoria Village | M4A, Toronto, ON, Canada, Toronto, ON M4A, CAN | 43.73064 | -79.31297 | 43.725882 | -79.315572 | 0.004758 | 0.002602 | 568.656678 | 2443.750415 | 71 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | M5A, Toronto, ON, Canada, Toronto, ON M5A, CAN | 43.65512 | -79.36264 | 43.654260 | -79.360636 | 0.000860 | -0.002004 | 187.801580 | 1115.710823 | 15 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | M6A, Toronto, ON, Canada, Toronto, ON M6A, CAN | 43.72329 | -79.45099 | 43.718518 | -79.464763 | 0.004772 | 0.013773 | 1230.009537 | 1827.071490 | 40 |
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government | M7A, Toronto, ON, Canada, Toronto, ON M7A, CAN | 43.66189 | -79.39172 | 43.662301 | -79.389494 | -0.000411 | -0.002226 | 185.294364 | 824.268954 | 79 |
import matplotlib.pyplot as plt
%matplotlib inline
ax = df_d['NN Distance'].plot(kind='hist', bins=np.arange(0,3750,250), width=200)
plt.yticks(np.arange(0,22,2));
plt.xlabel('Minimum Distance [meters]')
plt.title('Toronto Postal Code Nearest-Neighbor Distances')
Text(0.5, 1.0, 'Toronto Postal Code Nearest-Neighbor Distances')
Large separations make sense for some of the larger neighborhoods, but is there something causing the short distances?
Let's inspect all postal codes with separation less than 'raidus' meters:
radius = 500
showalldf(df_d[df_d['NN Distance']<radius])
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | GT Latitude | GT Longitude | Diff Lat | Diff Lon | GT Diff Meters | NN Distance | NN Index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | M5C | Downtown Toronto | St. James Town | M5C, Toronto, ON, Canada, Toronto, ON M5C, CAN | 43.65190 | -79.37574 | 43.651494 | -79.375418 | 0.000406 | -0.000322 | 52.067561 | 477.737684 | 48 |
| 30 | M5H | Downtown Toronto | Richmond, Adelaide, King | M5H, Toronto, ON, Canada, Toronto, ON M5H, CAN | 43.64991 | -79.38296 | 43.650571 | -79.384568 | -0.000661 | 0.001608 | 149.048218 | 247.021834 | 95 |
| 36 | M5J | Downtown Toronto | Harbourfront East, Union Station, Toronto Islands | M5J, Toronto, ON, Canada, Toronto, ON M5J, CAN | 43.64325 | -79.38062 | 43.640816 | -79.381752 | 0.002434 | 0.001132 | 285.477634 | 434.915762 | 42 |
| 42 | M5K | Downtown Toronto | Toronto Dominion Centre, Design Exchange | M5K, Toronto, ON, Canada, Toronto, ON M5K, CAN | 43.64712 | -79.38143 | 43.647177 | -79.381576 | -0.000057 | 0.000146 | 13.391742 | 95.554018 | 95 |
| 48 | M5L | Downtown Toronto | Commerce Court, Victoria Hotel | M5L, Toronto, ON, Canada, Toronto, ON M5L, CAN | 43.64832 | -79.37902 | 43.648198 | -79.379817 | 0.000121 | 0.000797 | 65.694621 | 198.863241 | 95 |
| 95 | M5X | Downtown Toronto | First Canadian Place, Underground city | M5X, Toronto, ON, Canada, Toronto, ON M5X, CAN | 43.64798 | -79.38144 | 43.648429 | -79.382280 | -0.000449 | 0.000840 | 84.177334 | 95.554018 | 42 |
As could be expected, Downtown Toronto is the densest area of the city. First Canadian Place and Underground city are an office building and an extensive network of underground tunnels and shops - a hiccup for Euclidian analysis. What happens if we drop that neighborhood?
ind_remove = 95
dist_matrix = [[e for j, e in enumerate(d) if j!=ind_remove] for i, d in enumerate(dist_matrix) if i!=ind_remove]
dist_nn = [min([x for x in y if x > 0]) for y in dist_matrix]
dist_nn_index = [np.argmin(np.array([(9999999 if x==0 else x) for x in d])) for d in dist_matrix]
df_e = df_d.copy()
df_e.drop(index=95, inplace=True)
df_e.reset_index(drop=True, inplace=True)
df_e['NN Distance'] = dist_nn
df_e['NN Index'] = dist_nn_index
df_e.tail()
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | GT Latitude | GT Longitude | Diff Lat | Diff Lon | GT Diff Meters | NN Distance | NN Index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 94 | M4X | Downtown Toronto | St. James Town, Cabbagetown | M4X, Toronto, ON, Canada, Toronto, ON M4X, CAN | 43.66788 | -79.36649 | 43.667967 | -79.367675 | -0.000087 | 0.001185 | 96.084488 | 1205.443164 | 96 |
| 95 | M8X | Etobicoke | The Kingsway, Montgomery Road, Old Mill North | M8X, Toronto, ON, Canada, Toronto, ON M8X, CAN | 43.65322 | -79.51114 | 43.653654 | -79.506944 | -0.000434 | -0.004196 | 341.940892 | 1734.688927 | 5 |
| 96 | M4Y | Downtown Toronto | Church and Wellesley | M4Y, Toronto, ON, Canada, Toronto, ON M4Y, CAN | 43.66659 | -79.38133 | 43.665860 | -79.383160 | 0.000730 | 0.001830 | 168.412786 | 987.410477 | 4 |
| 97 | M8Y | Etobicoke | Old Mill South, King's Mill Park, Sunnylea, Hu... | M8Y, Toronto, ON, Canada, Toronto, ON M8Y, CAN | 43.63299 | -79.48968 | 43.636258 | -79.498509 | -0.003268 | 0.008829 | 799.659159 | 2216.522451 | 80 |
| 98 | M8Z | Etobicoke | Mimico NW, The Queensway West, South of Bloor,... | M8Z, Toronto, ON, Canada, Toronto, ON M8Z, CAN | 43.62517 | -79.52703 | 43.628841 | -79.520999 | -0.003671 | -0.006031 | 634.999798 | 2889.063851 | 87 |
ax = df_e['NN Distance'].plot(kind='hist', bins=np.arange(0,3750,250), width=200)
plt.yticks(np.arange(0,22,2));
plt.xlabel('Minimum Distance [meters]')
plt.title('Toronto Postal Code Nearest-Neighbor Distances')
Text(0.5, 1.0, 'Toronto Postal Code Nearest-Neighbor Distances')
radius = 500
showalldf(df_e[df_e['NN Distance']<radius])
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | GT Latitude | GT Longitude | Diff Lat | Diff Lon | GT Diff Meters | NN Distance | NN Index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | M5C | Downtown Toronto | St. James Town | M5C, Toronto, ON, Canada, Toronto, ON M5C, CAN | 43.65190 | -79.37574 | 43.651494 | -79.375418 | 0.000406 | -0.000322 | 52.067561 | 477.737684 | 48 |
| 30 | M5H | Downtown Toronto | Richmond, Adelaide, King | M5H, Toronto, ON, Canada, Toronto, ON M5H, CAN | 43.64991 | -79.38296 | 43.650571 | -79.384568 | -0.000661 | 0.001608 | 149.048218 | 333.656904 | 42 |
| 36 | M5J | Downtown Toronto | Harbourfront East, Union Station, Toronto Islands | M5J, Toronto, ON, Canada, Toronto, ON M5J, CAN | 43.64325 | -79.38062 | 43.640816 | -79.381752 | 0.002434 | 0.001132 | 285.477634 | 434.915762 | 42 |
| 42 | M5K | Downtown Toronto | Toronto Dominion Centre, Design Exchange | M5K, Toronto, ON, Canada, Toronto, ON M5K, CAN | 43.64712 | -79.38143 | 43.647177 | -79.381576 | -0.000057 | 0.000146 | 13.391742 | 235.757645 | 48 |
| 48 | M5L | Downtown Toronto | Commerce Court, Victoria Hotel | M5L, Toronto, ON, Canada, Toronto, ON M5L, CAN | 43.64832 | -79.37902 | 43.648198 | -79.379817 | 0.000121 | 0.000797 | 65.694621 | 235.757645 | 42 |
So there are still relatively small distances between neighborhoods in downtown Toronto. Let's stay with the original list anyway.
For feature generation, we can consider that larger areas (more separated neighborhoods) are probably less dense, and to get a sampling of amenities we may need to query on larger distances.
But before moving on to that, let's add a couple of extra interesting features not from Foursquare, such as population density.
We collect some simple features from the 2016 census:
Begin by loading the relevant data into a dataframe:
def urlToFile(url, filename, overwrite=True, verbose=False):
'''Downloads from a url and saves it to filename
Returns True if file was written, False otherwise
'''
try:
path = os.path.dirname(filename) if os.path.isabs(filename) else os.path.dirname(os.path.join(os.getcwd(),filename))
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
if os.path.isfile(filename) and not overwrite:
if verbose: print('File exists and will not be overwritten')
return False
response = requests.get(url)
if response.status_code==200:
with open(filename,'w+b') as f:
for chunk in response:
f.write(chunk)
if verbose: print('File saved from url')
return True
if verbose: print('File not downloaded. Status code:',response.status_code)
return False
except BaseException as e:
if verbose: print('Error saving file from url:',e)
return False
census_url = 'https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/hlt-fst/pd-pl/Tables/CompFile.cfm?Lang=Eng&T=1201&OFT=FULLCSV'
urlToFile(census_url,'T1201EN.csv',overwrite=False,verbose=True);
File exists and will not be overwritten
area_url = 'https://mdl.library.utoronto.ca/sites/default/public/mdldata/open/canada/national/statcan/geo/2016/ref_products/ut/fsa_area_2016.csv'
urlToFile(area_url,'fsa_area_2016.csv',overwrite=False,verbose=True);
File exists and will not be overwritten
# 2016 Census data: FSA population and dwelling number, downloaded from https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/hlt-fst/pd-pl/comprehensive.cfm
df_fsa_population = pd.read_csv('T1201EN.csv',header=0,skiprows=[1],skipfooter=8,engine='python')
df_fsa_population.rename(columns={'Geographic code':'Postal Code','Population, 2016':'Population','Total private dwellings, 2016':'Total Dwellings','Private dwellings occupied by usual residents, 2016':'Occupied Dwellings'},inplace=True)
df_fsa_population = df_fsa_population[['Postal Code','Population','Total Dwellings','Occupied Dwellings']]
# 2016 Census data: FSA area in square kilometers, downloaded from https://mdl.library.utoronto.ca/collections/numeric-data/census-canada/2016/geo
df_fsa_area = pd.read_csv('fsa_area_2016.csv',header=1)
df_fsa_area.rename(columns={'CFSAUID':'Postal Code','Area Sq Km':'Square km'},inplace=True)
df_fsa_area = df_fsa_area[['Postal Code','Square km']]
df_g = df_c.merge(df_fsa_population,how='left',on='Postal Code')
df_g = df_g.merge(df_fsa_area,how='left',on='Postal Code')
print('Dataframe shape:',df_g.shape)
df_g.head()
Dataframe shape: (100, 10)
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | Population | Total Dwellings | Occupied Dwellings | Square km | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.32760 | 34615 | 13847 | 13241 | 7.818049 |
| 1 | M4A | North York | Victoria Village | M4A, Toronto, ON, Canada, Toronto, ON M4A, CAN | 43.73064 | -79.31297 | 14443 | 6299 | 6170 | 4.723358 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | M5A, Toronto, ON, Canada, Toronto, ON M5A, CAN | 43.65512 | -79.36264 | 41078 | 24186 | 22333 | 4.297526 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | M6A, Toronto, ON, Canada, Toronto, ON M6A, CAN | 43.72329 | -79.45099 | 21048 | 8751 | 8074 | 5.829964 |
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government | M7A, Toronto, ON, Canada, Toronto, ON M7A, CAN | 43.66189 | -79.39172 | 10 | 6 | 5 | NaN |
Let's examine any missing values.
display(df_g.loc[df_g['Square km'].isnull(),:])
print(f"Missing values at indices: {df_g.loc[df_g['Square km'].isnull(),:].index.to_list()}")
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | Population | Total Dwellings | Occupied Dwellings | Square km | |
|---|---|---|---|---|---|---|---|---|---|---|
| 4 | M7A | Downtown Toronto | Queen's Park, Ontario Provincial Government | M7A, Toronto, ON, Canada, Toronto, ON M7A, CAN | 43.66189 | -79.39172 | 10 | 6 | 5 | NaN |
| 42 | M5K | Downtown Toronto | Toronto Dominion Centre, Design Exchange | M5K, Toronto, ON, Canada, Toronto, ON M5K, CAN | 43.64712 | -79.38143 | 0 | 1 | 1 | NaN |
| 48 | M5L | Downtown Toronto | Commerce Court, Victoria Hotel | M5L, Toronto, ON, Canada, Toronto, ON M5L, CAN | 43.64832 | -79.37902 | 0 | 1 | 1 | NaN |
| 95 | M5X | Downtown Toronto | First Canadian Place, Underground city | M5X, Toronto, ON, Canada, Toronto, ON M5X, CAN | 43.64798 | -79.38144 | 10 | 5 | 3 | NaN |
Missing values at indices: [4, 42, 48, 95]
df_h = df_g.copy(deep=True)
df_h.drop(index=df_g.loc[df_g['Square km'].isnull(),:].index, inplace=True)
df_h.reset_index( drop=True, inplace=True)
print('Dataframe shape:',df_h.shape)
df_h.head()
Dataframe shape: (96, 10)
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | Population | Total Dwellings | Occupied Dwellings | Square km | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.32760 | 34615 | 13847 | 13241 | 7.818049 |
| 1 | M4A | North York | Victoria Village | M4A, Toronto, ON, Canada, Toronto, ON M4A, CAN | 43.73064 | -79.31297 | 14443 | 6299 | 6170 | 4.723358 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | M5A, Toronto, ON, Canada, Toronto, ON M5A, CAN | 43.65512 | -79.36264 | 41078 | 24186 | 22333 | 4.297526 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | M6A, Toronto, ON, Canada, Toronto, ON M6A, CAN | 43.72329 | -79.45099 | 21048 | 8751 | 8074 | 5.829964 |
| 4 | M9A | Etobicoke | Islington Avenue, Humber Valley Village | M9A, Toronto, ON, Canada, Toronto, ON M9A, CAN | 43.66263 | -79.52830 | 35594 | 15730 | 15119 | 10.651703 |
So four more irrelevant postal codes are found. Note that the previous three would appear in this list as well, as they also have no entry for land area.
Let's drop these four new rows, since they are clearly not relevant from a residential standpoint. It could be argued that there may be residences nearby, but as noted previously for Downtown Toronto, the density is high and the remaining postal codes should capture any neighborhood differences.
We should use the new population, dwelling, and area features to compute features more suitable for comparison:
df_h['Population Density'] = df_h['Population']/df_h['Square km']
df_h['Residentiality'] = df_h['Occupied Dwellings']/df_h['Total Dwellings']
df_h['Dwelling Density'] = df_h['Population']/df_h['Occupied Dwellings']
df_h.head(1)
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | Population | Total Dwellings | Occupied Dwellings | Square km | Population Density | Residentiality | Dwelling Density | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.3276 | 34615 | 13847 | 13241 | 7.818049 | 4427.575256 | 0.956236 | 2.614229 |
Let's visualize how these new features are distributed:
fig, axs = plt.subplots(2,3,figsize=(17,10))
ax = axs[0][0]
ax.hist(df_h['Population Density'],bins=20)
ax.set_xlabel('Population Density [Persons/km$^2$]')
ax.set_ylabel('Count')
ax = axs[1][0]
ax.hist(df_h['Population Density'].apply(np.log10),bins=20)
ax.set_xlabel('Log$_{10}$ Population Density [Persons/km$^2$]')
ax.set_ylabel('Count')
ax = axs[0][1]
ax.hist(df_h['Dwelling Density'],bins=20)
ax.set_xlabel('Dwelling Density [Persons/Usually Occupied Dwelling]')
ax.set_ylabel('Count')
ax = axs[1][1]
ax.hist(df_h['Dwelling Density'].apply(np.log10),bins=20)
ax.set_xlabel('Log$_{10}$ Dwelling Density [Persons/Usually Occupied Dwelling]')
ax.set_ylabel('Count')
ax = axs[0][2]
ax.hist(df_h['Residentiality'],bins=20)
ax.set_xlabel('Residential to Total Dwelling Ratio []')
ax.set_ylabel('Count')
ax = axs[1][2]
ax.hist((1-df_h['Residentiality']).apply(np.log10),bins=20)
ax.set_xlabel('Log$_{10}$ (1- Residential to Total Dwelling Ratio) []')
ax.set_ylabel('Count');
So that our variables are near-normally distributed, we should keep log$_{10}$ Population Density, unmodified Dwelling Density, and log$_{10}$ of 1 - the Residential to Total Dwelling Ratio. The ranges will be adjusted using scalers during the classification. Interestingly, the Population Density shows some evidence of clustering (though with only single low bins between the peaks, this may just be an artifact.
Let's construct and keep those variables in the dataframe:
df_h['Log10 (Population Density)'] = df_h['Population Density'].apply(np.log10)
df_h['Log10 (1 - Residentiality)'] = (1-df_h['Residentiality']).apply(np.log10)
df_h.head(1)
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | Population | Total Dwellings | Occupied Dwellings | Square km | Population Density | Residentiality | Dwelling Density | Log10 (Population Density) | Log10 (1 - Residentiality) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.3276 | 34615 | 13847 | 13241 | 7.818049 | 4427.575256 | 0.956236 | 2.614229 | 3.646166 | -1.358883 |
Let's also recompute the nearest neighbor distance and index, since the number of rows has been reduced since we last did that.
def getNN(lat,lon):
# Returns the NN distance in meters and index of NN given input arrays of latitude and longitude as Pandas series
assert len(lat)==len(lon)
dist_matrix = []
for i in range(len(lat)):
# This implementation is slow by a factor of 2, look into distance matrix functions
dist_matrix.append([distance.distance((lat[i],lon[i]),(lat[j],lon[j])).meters for j in range(len(lat))])
nn_dist = pd.Series(data=[min([x for x in y if x > 0]) for y in dist_matrix],name='NN Distance [m]')
nn_index = pd.Series(data=[np.argmin(np.array([(9999999 if x==0 else x) for x in d])) for d in dist_matrix],name='NN Index',dtype=int)
return nn_dist, nn_index
df_h = pd.concat([df_h,*getNN(df_h['Latitude'],df_h['Longitude'])],axis=1)
df_h.head()
| Postal Code | Borough | Neighbourhood | GString | Latitude | Longitude | Population | Total Dwellings | Occupied Dwellings | Square km | Population Density | Residentiality | Dwelling Density | Log10 (Population Density) | Log10 (1 - Residentiality) | NN Distance [m] | NN Index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | M3A, Toronto, ON, Canada, Toronto, ON M3A, CAN | 43.74998 | -79.32760 | 34615 | 13847 | 13241 | 7.818049 | 4427.575256 | 0.956236 | 2.614229 | 3.646166 | -1.358883 | 2179.358574 | 68 |
| 1 | M4A | North York | Victoria Village | M4A, Toronto, ON, Canada, Toronto, ON M4A, CAN | 43.73064 | -79.31297 | 14443 | 6299 | 6170 | 4.723358 | 3057.782424 | 0.979521 | 2.340843 | 3.485407 | -1.688682 | 2443.750415 | 68 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | M5A, Toronto, ON, Canada, Toronto, ON M5A, CAN | 43.65512 | -79.36264 | 41078 | 24186 | 22333 | 4.297526 | 9558.522031 | 0.923385 | 1.839341 | 3.980391 | -1.115689 | 1115.710823 | 14 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | M6A, Toronto, ON, Canada, Toronto, ON M6A, CAN | 43.72329 | -79.45099 | 21048 | 8751 | 8074 | 5.829964 | 3610.313875 | 0.922637 | 2.606886 | 3.557545 | -1.111469 | 1827.071490 | 39 |
| 4 | M9A | Etobicoke | Islington Avenue, Humber Valley Village | M9A, Toronto, ON, Canada, Toronto, ON M9A, CAN | 43.66263 | -79.52830 | 35594 | 15730 | 15119 | 10.651703 | 3341.625250 | 0.961157 | 2.354256 | 3.523958 | -1.410688 | 1734.688927 | 92 |
And the distribution of neighborhood distances is now:
ax = df_h['NN Distance [m]'].plot(kind='hist', bins=np.arange(0,3750,250), width=200)
plt.yticks(np.arange(0,22,2))
plt.xlabel('Minimum Distance [meters]')
plt.title('Toronto Postal Code Nearest-Neighbor Distances');
So those close neighborhood distances had been due to zero-area postal codes. Now neighborhoods are separated by at least 500 meters, which makes sense for a length scale for city characteristics to change between (a 5 minute walk).
Let's visualize our final dataframe for this section, sticking with the values from the HERE API lookup (in df_c), dropping the GString as it has served its purpose for validation, and keeping the nearest neighbor information as it might prove useful in the next section.
df_final = df_h.copy(deep=True)
df_final.drop(columns=['GString','Population','Total Dwellings','Occupied Dwellings','Square km','Population Density','Residentiality'],inplace=True)
df_final.head()
| Postal Code | Borough | Neighbourhood | Latitude | Longitude | Dwelling Density | Log10 (Population Density) | Log10 (1 - Residentiality) | NN Distance [m] | NN Index | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | 43.74998 | -79.32760 | 2.614229 | 3.646166 | -1.358883 | 2179.358574 | 68 |
| 1 | M4A | North York | Victoria Village | 43.73064 | -79.31297 | 2.340843 | 3.485407 | -1.688682 | 2443.750415 | 68 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | 43.65512 | -79.36264 | 1.839341 | 3.980391 | -1.115689 | 1115.710823 | 14 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | 43.72329 | -79.45099 | 2.606886 | 3.557545 | -1.111469 | 1827.071490 | 39 |
| 4 | M9A | Etobicoke | Islington Avenue, Humber Valley Village | 43.66263 | -79.52830 | 2.354256 | 3.523958 | -1.410688 | 1734.688927 | 92 |
import folium
centroidlatlon = [df_final['Latitude'].mean(), df_final['Longitude'].mean()]
torontolatlon = gc.geocode("Toronto, Ontario")[1]
map_toronto = folium.Map(location=centroidlatlon, zoom_start=11) # generate map centred around the center of postal codes
# add a red circle marker to represent the center of neighborhoods
folium.CircleMarker(
centroidlatlon,
radius=10,
color='green',
popup='Centroid of Toronto',
tooltip='Centroid of Toronto',
fill = True,
fill_color = 'green',
fill_opacity = 0.6
).add_to(map_toronto)
folium.CircleMarker(
torontolatlon,
radius=15,
color='red',
popup='Center of Toronto',
tooltip='Center of Toronto',
fill = True,
fill_color = 'red',
fill_opacity = 0.6
).add_to(map_toronto)
for i, row in df_final[['Postal Code','Latitude','Longitude']].iterrows():
folium.CircleMarker(
[row['Latitude'], row['Longitude']],
radius=5,
color='blue',
popup=row['Postal Code'],
tooltip=row['Postal Code'],
fill = True,
fill_color = 'blue',
fill_opacity = 0.6
).add_to(map_toronto)
map_toronto
Finally, let's view the final dataframe.
showalldf(df_final)
| Postal Code | Borough | Neighbourhood | Latitude | Longitude | Dwelling Density | Log10 (Population Density) | Log10 (1 - Residentiality) | NN Distance [m] | NN Index | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | North York | Parkwoods | 43.74998 | -79.32760 | 2.614229 | 3.646166 | -1.358883 | 2179.358574 | 68 |
| 1 | M4A | North York | Victoria Village | 43.73064 | -79.31297 | 2.340843 | 3.485407 | -1.688682 | 2443.750415 | 68 |
| 2 | M5A | Downtown Toronto | Regent Park, Harbourfront | 43.65512 | -79.36264 | 1.839341 | 3.980391 | -1.115689 | 1115.710823 | 14 |
| 3 | M6A | North York | Lawrence Manor, Lawrence Heights | 43.72329 | -79.45099 | 2.606886 | 3.557545 | -1.111469 | 1827.071490 | 39 |
| 4 | M9A | Etobicoke | Islington Avenue, Humber Valley Village | 43.66263 | -79.52830 | 2.354256 | 3.523958 | -1.410688 | 1734.688927 | 92 |
| 5 | M1B | Scarborough | Malvern, Rouge | 43.81186 | -79.19672 | 3.267820 | 3.246560 | -1.459795 | 2933.985295 | 90 |
| 6 | M3B | North York | Don Mills | 43.74945 | -79.36160 | 2.664267 | 3.343089 | -1.432117 | 2064.820849 | 43 |
| 7 | M4B | East York | Parkview Hill, Woodbine Gardens | 43.70732 | -79.31173 | 2.451375 | 3.555357 | -1.459922 | 1882.044457 | 13 |
| 8 | M5B | Downtown Toronto | Garden District, Ryerson | 43.65749 | -79.37849 | 1.811420 | 4.311036 | -0.840490 | 542.283390 | 23 |
| 9 | M6B | North York | Glencairn | 43.70687 | -79.44898 | 2.658650 | 3.707978 | -1.245047 | 1831.548761 | 3 |
| 10 | M9B | Etobicoke | West Deane Park, Princess Gardens, Martin Grov... | 43.65033 | -79.55371 | 2.635861 | 3.364382 | -1.589073 | 1989.445095 | 16 |
| 11 | M1C | Scarborough | Rouge Hill, Port Union, Highland Creek | 43.78562 | -79.15871 | 3.160014 | 3.382638 | -1.567079 | 2555.618897 | 17 |
| 12 | M3C | North York | Don Mills | 43.72171 | -79.34358 | 2.470065 | 3.577046 | -1.667909 | 2113.749213 | 22 |
| 13 | M4C | East York | Woodbine Heights | 43.69069 | -79.30729 | 2.411919 | 3.953290 | -1.431558 | 1827.302426 | 18 |
| 14 | M5C | Downtown Toronto | St. James Town | 43.65190 | -79.37574 | 1.714701 | 3.992841 | -1.075270 | 623.025076 | 29 |
| 15 | M6C | York | Humewood-Cedarvale | 43.69207 | -79.43051 | 2.346051 | 3.890237 | -1.295675 | 1333.453596 | 65 |
| 16 | M9C | Etobicoke | Eringate, Bloordale Gardens, Old Burnhamthorpe... | 43.64857 | -79.57825 | 2.533982 | 3.404320 | -1.723684 | 1989.445095 | 10 |
| 17 | M1E | Scarborough | Guildwood, Morningside, West Hill | 43.76575 | -79.17470 | 2.735447 | 3.458327 | -1.568818 | 2555.618897 | 11 |
| 18 | M4E | East Toronto | The Beaches | 43.67657 | -79.29567 | 2.322329 | 3.807506 | -1.353493 | 1801.103731 | 45 |
| 19 | M5E | Downtown Toronto | Berczy Park | 43.64516 | -79.37356 | 1.604717 | 4.306696 | -1.054265 | 607.873587 | 35 |
| 20 | M6E | York | Caledonia-Fairbanks | 43.68788 | -79.45026 | 2.584132 | 3.859803 | -1.162276 | 1658.955655 | 15 |
| 21 | M1G | Scarborough | Woburn | 43.77144 | -79.21499 | 3.039828 | 3.495048 | -1.462183 | 1911.114131 | 25 |
| 22 | M4G | East York | Leaside | 43.70977 | -79.36400 | 2.467469 | 3.441715 | -1.332572 | 1503.576172 | 28 |
| 23 | M5G | Downtown Toronto | Central Bay Street | 43.65609 | -79.38493 | 1.708866 | 4.083384 | -0.792732 | 542.283390 | 8 |
| 24 | M6G | Downtown Toronto | Christie | 43.66869 | -79.42071 | 2.390197 | 3.903393 | -1.207287 | 1473.665131 | 71 |
| 25 | M1H | Scarborough | Cedarbrae | 43.76929 | -79.23854 | 2.713745 | 3.677686 | -1.506369 | 1911.114131 | 21 |
| 26 | M2H | North York | Hillcrest Village | 43.80225 | -79.35709 | 2.815747 | 3.483570 | -1.364315 | 2489.523753 | 32 |
| 27 | M3H | North York | Bathurst Manor, Wilson Heights, Downsview North | 43.75788 | -79.44847 | 2.601645 | 3.347735 | -1.331813 | 2217.458987 | 69 |
| 28 | M4H | East York | Thorncliffe Park | 43.70142 | -79.34932 | 3.069057 | 4.230637 | -2.033089 | 1503.576172 | 22 |
| 29 | M5H | Downtown Toronto | Richmond, Adelaide, King | 43.64991 | -79.38296 | 1.613033 | 3.606783 | -0.558330 | 623.025076 | 14 |
| 30 | M6H | West Toronto | Dufferin, Dovercourt Village | 43.66481 | -79.43904 | 2.340049 | 3.963639 | -1.178483 | 1539.953316 | 24 |
| 31 | M1J | Scarborough | Scarborough Village | 43.74450 | -79.23145 | 2.989979 | 3.755891 | -1.388606 | 2339.291649 | 48 |
| 32 | M2J | North York | Fairview, Henry Farm, Oriole | 43.78080 | -79.34815 | 2.664701 | 3.740178 | -1.541071 | 2489.523753 | 26 |
| 33 | M3J | North York | Northwood Park, York University | 43.76469 | -79.48749 | 2.707877 | 3.267093 | -1.111353 | 2725.995973 | 57 |
| 34 | M4J | East York | East Toronto, Broadview North (Old East York) | 43.68811 | -79.33417 | 2.422258 | 3.805624 | -1.438398 | 1917.963485 | 28 |
| 35 | M5J | Downtown Toronto | Harbourfront East, Union Station, Toronto Islands | 43.64325 | -79.38062 | 1.681697 | 3.519698 | -0.894458 | 607.873587 | 19 |
| 36 | M6J | West Toronto | Little Portugal, Trinity | 43.64873 | -79.41784 | 2.169820 | 4.037766 | -1.109262 | 1161.465726 | 41 |
| 37 | M1K | Scarborough | Kennedy Park, Ionview, East Birchmount Park | 43.72466 | -79.26505 | 2.701283 | 3.725081 | -1.431131 | 2093.106811 | 42 |
| 38 | M2K | North York | Bayview Village | 43.77949 | -79.38050 | 2.262998 | 3.598582 | -1.263969 | 2608.296602 | 32 |
| 39 | M3K | North York | Downsview | 43.73393 | -79.46828 | 2.716033 | 3.525328 | -1.446022 | 1827.071490 | 3 |
| 40 | M4K | East Toronto | The Danforth West, Riverdale | 43.68284 | -79.35761 | 2.169758 | 3.864125 | -1.374251 | 1590.287466 | 87 |
| 41 | M6K | West Toronto | Brockton, Parkdale Village, Exhibition Place | 43.63941 | -79.42436 | 1.859738 | 4.145905 | -1.247272 | 1161.465726 | 36 |
| 42 | M1L | Scarborough | Golden Mile, Clairlea, Oakridge | 43.71264 | -79.28505 | 2.822739 | 3.604278 | -1.451086 | 2093.106811 | 37 |
| 43 | M2L | North York | York Mills, Silver Hills | 43.73933 | -79.38310 | 2.948415 | 3.259913 | -1.363194 | 1306.020983 | 58 |
| 44 | M3L | North York | Downsview | 43.74060 | -79.51012 | 2.977163 | 3.460733 | -0.903090 | 1187.528137 | 50 |
| 45 | M4L | East Toronto | India Bazaar, The Beaches West | 43.66805 | -79.31467 | 2.348033 | 3.847293 | -1.300259 | 1764.492695 | 51 |
| 46 | M6L | North York | North Park, Maple Leaf Park, Upwood Park | 43.71390 | -79.48875 | 2.790849 | 3.604791 | -1.531194 | 2114.903379 | 53 |
| 47 | M9L | North York | Humber Summit | 43.75948 | -79.55690 | 3.190069 | 3.156393 | -1.039920 | 2880.678411 | 85 |
| 48 | M1M | Scarborough | Cliffside, Cliffcrest, Scarborough Village West | 43.72360 | -79.23496 | 2.657196 | 3.382544 | -1.494935 | 2339.291649 | 31 |
| 49 | M2M | North York | Willowdale, Newtonbrook | 43.79209 | -79.41381 | 2.606662 | 3.605925 | -1.279850 | 2992.833847 | 69 |
| 50 | M3M | North York | Downsview | 43.73202 | -79.50133 | 2.604072 | 3.661784 | -1.313098 | 1187.528137 | 44 |
| 51 | M4M | East Toronto | Studio District | 43.66213 | -79.33497 | 2.288774 | 3.418213 | -1.274165 | 1764.492695 | 45 |
| 52 | M5M | North York | Bedford Park, Lawrence Manor East | 43.73550 | -79.41917 | 2.760948 | 3.680598 | -1.373195 | 2045.983462 | 63 |
| 53 | M6M | York | Del Ray, Mount Dennis, Keelsdale and Silverthorn | 43.69525 | -79.48350 | 2.648814 | 3.683275 | -1.333746 | 2114.903379 | 46 |
| 54 | M9M | North York | Humberlea, Emery | 43.73406 | -79.53680 | 3.045554 | 3.513408 | -1.239386 | 2268.827888 | 44 |
| 55 | M1N | Scarborough | Birch Cliff, Cliffside West | 43.69470 | -79.26502 | 2.433865 | 3.550480 | -1.335868 | 2565.086544 | 42 |
| 56 | M2N | North York | Willowdale, Willowdale East | 43.76382 | -79.41393 | 2.247801 | 3.879735 | -1.185240 | 2085.199782 | 63 |
| 57 | M3N | North York | Downsview | 43.75690 | -79.51959 | 3.031428 | 3.779127 | -1.328272 | 1965.118084 | 44 |
| 58 | M4N | Central Toronto | Lawrence Park | 43.72795 | -79.38716 | 2.476575 | 3.475487 | -1.325574 | 1306.020983 | 43 |
| 59 | M5N | Central Toronto | Roselawn | 43.71208 | -79.41848 | 2.560900 | 3.759931 | -1.232851 | 967.184564 | 70 |
| 60 | M6N | York | Runnymede, The Junction North | 43.67596 | -79.48337 | 2.538031 | 3.671298 | -1.241316 | 2143.262400 | 53 |
| 61 | M9N | York | Weston | 43.70523 | -79.51985 | 2.465244 | 3.692723 | -1.295912 | 1248.911068 | 67 |
| 62 | M1P | Scarborough | Dorset Park, Wexford Heights, Scarborough Town... | 43.76067 | -79.26891 | 2.755200 | 3.585433 | -1.463617 | 2626.316709 | 25 |
| 63 | M2P | North York | York Mills West | 43.74785 | -79.40033 | 2.597020 | 3.414794 | -1.368666 | 1679.986550 | 43 |
| 64 | M4P | Central Toronto | Davisville North | 43.71269 | -79.38892 | 1.744494 | 3.973048 | -1.229276 | 1049.124320 | 75 |
| 65 | M5P | Central Toronto | Forest Hill North & West, Forest Hill Road Park | 43.69479 | -79.41440 | 2.077326 | 3.751335 | -1.141243 | 1333.453596 | 15 |
| 66 | M6P | West Toronto | High Park, The Junction South | 43.65936 | -79.46207 | 2.110215 | 3.961866 | -1.320740 | 1483.145263 | 77 |
| 67 | M9P | Etobicoke | Westmount | 43.69630 | -79.52926 | 2.640273 | 3.472775 | -1.448325 | 1248.911068 | 61 |
| 68 | M1R | Scarborough | Wexford, Maryvale | 43.75071 | -79.30056 | 2.773103 | 3.436592 | -1.474053 | 2179.358574 | 0 |
| 69 | M2R | North York | Willowdale, Willowdale West | 43.77770 | -79.44524 | 2.619069 | 3.738429 | -1.518271 | 2217.458987 | 27 |
| 70 | M4R | Central Toronto | North Toronto West, Lawrence Park | 43.71452 | -79.40696 | 2.262510 | 3.768071 | -1.255598 | 967.184564 | 59 |
| 71 | M5R | Central Toronto | The Annex, North Midtown, Yorkville | 43.67484 | -79.40452 | 1.898946 | 3.937855 | -0.935110 | 1228.682733 | 82 |
| 72 | M6R | West Toronto | Parkdale, Roncesvalles | 43.64758 | -79.44966 | 2.309491 | 3.906676 | -1.180835 | 1647.812254 | 66 |
| 73 | M9R | Etobicoke | Kingsview Village, St. Phillips, Martin Grove ... | 43.68681 | -79.55728 | 2.735327 | 3.681401 | -1.483466 | 2492.945963 | 67 |
| 74 | M1S | Scarborough | Agincourt | 43.79452 | -79.26711 | 2.987109 | 3.443583 | -1.379258 | 2590.727238 | 78 |
| 75 | M4S | Central Toronto | Davisville | 43.70340 | -79.38659 | 1.978503 | 3.914375 | -1.358301 | 1049.124320 | 64 |
| 76 | M5S | Downtown Toronto | University of Toronto, Harbord | 43.66311 | -79.40180 | 1.951257 | 3.883305 | -0.795533 | 1115.915978 | 80 |
| 77 | M6S | West Toronto | Runnymede, Swansea | 43.64982 | -79.47493 | 2.340430 | 3.586780 | -1.401110 | 1483.145263 | 66 |
| 78 | M1T | Scarborough | Clarks Corners, Tam O'Shanter, Sullivan | 43.78482 | -79.29638 | 2.671094 | 3.730489 | -1.478550 | 2590.727238 | 74 |
| 79 | M4T | Central Toronto | Moore Park, Summerhill East | 43.69048 | -79.38331 | 2.007483 | 3.774462 | -1.282402 | 1069.745491 | 87 |
| 80 | M5T | Downtown Toronto | Kensington Market, Chinatown, Grange Park | 43.65362 | -79.39727 | 2.078950 | 4.047019 | -1.002297 | 1032.598900 | 23 |
| 81 | M1V | Scarborough | Milliken, Agincourt North, Steeles East, L'Amo... | 43.81781 | -79.28024 | 3.397962 | 3.562977 | -1.663471 | 2795.090184 | 74 |
| 82 | M4V | Central Toronto | Summerhill West, Rathnelly, South Hill, Forest... | 43.68575 | -79.40203 | 1.887130 | 3.804353 | -1.205639 | 1228.682733 | 71 |
| 83 | M5V | Downtown Toronto | CN Tower, King and Spadina, Railway Lands, Har... | 43.64082 | -79.39818 | 1.601087 | 4.108762 | -0.908099 | 1424.042426 | 80 |
| 84 | M8V | Etobicoke | New Toronto, Mimico South, Humber Bay Shores | 43.60987 | -79.49809 | 1.937401 | 3.735701 | -0.921658 | 2656.920647 | 94 |
| 85 | M9V | Etobicoke | South Steeles, Silverstone, Humbergate, Jamest... | 43.74371 | -79.58529 | 3.329307 | 3.529189 | -1.352059 | 2880.678411 | 47 |
| 86 | M1W | Scarborough | Steeles West, L'Amoreaux West | 43.80077 | -79.32097 | 2.997032 | 3.741623 | -1.592664 | 2656.600714 | 78 |
| 87 | M4W | Downtown Toronto | Rosedale | 43.68190 | -79.37729 | 2.003715 | 3.488057 | -1.085035 | 1069.745491 | 79 |
| 88 | M8W | Etobicoke | Alderwood, Long Branch | 43.60074 | -79.54173 | 2.357355 | 3.452371 | -1.369766 | 2962.353592 | 95 |
| 89 | M9W | Etobicoke | Northwest, West Humber - Clairville | 43.71209 | -79.57854 | 2.968768 | 3.149611 | -1.267217 | 3290.316715 | 73 |
| 90 | M1X | Scarborough | Upper Rouge | 43.83398 | -79.21664 | 4.137298 | 3.307858 | -1.618513 | 2933.985295 | 5 |
| 91 | M4X | Downtown Toronto | St. James Town, Cabbagetown | 43.66788 | -79.36649 | 2.020376 | 4.233803 | -1.332218 | 1205.443164 | 93 |
| 92 | M8X | Etobicoke | The Kingsway, Montgomery Road, Old Mill North | 43.65322 | -79.51114 | 2.384922 | 3.541080 | -1.474346 | 1734.688927 | 4 |
| 93 | M4Y | Downtown Toronto | Church and Wellesley | 43.66659 | -79.38133 | 1.549555 | 4.475279 | -0.978317 | 1036.687158 | 8 |
| 94 | M8Y | Etobicoke | Old Mill South, King's Mill Park, Sunnylea, Hu... | 43.63299 | -79.48968 | 2.177367 | 3.566945 | -1.173613 | 2216.522451 | 77 |
| 95 | M8Z | Etobicoke | Mimico NW, The Queensway West, South of Bloor,... | 43.62517 | -79.52703 | 2.512238 | 3.266416 | -1.426339 | 2889.063851 | 84 |
We begin by loading Foursquare credentials and defining variables necessary for our queries.
import config
import importlib
importlib.reload(config)
CLIENT_ID = config.FOURSQUARE_CLIENT_ID
CLIENT_SECRET = config.FOURSQUARE_CLIENT_SECRET
ACCESS_TOKEN = config.FOURSQUARE_ACCESS_TOKEN
VERSION = '20180604'
LIMIT = 100
RADIUS = 500
We now define some utility functions for data acquisition and plotting.
import requests
# Queries a location to find nearby venues, uses session variables CLIENT_ID, CLIENT_SECRET, VERSION, LIMIT
def FoursquareExplore(latitude, longitude, radius):
url = 'https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&ll={},{}&v={}&radius={}&limit={}'.format(
CLIENT_ID, CLIENT_SECRET, latitude, longitude, VERSION, radius, LIMIT)
results = requests.get(url).json()
if results['response']=={}:
if results['meta']['errorDetail']=='Quota exceeded':
print('Foursquare quota exceeded, please try again later')
else:
print('No results returned')
print(results)
items = results['response']['groups'][0]['items']
if len(items)>0:
dataframe = pd.json_normalize(items)
filtered_columns = ['venue.name', 'venue.categories'] + [col for col in dataframe.columns if col.startswith('venue.location.')] + ['venue.id']
dataframe_filtered = dataframe.loc[:, filtered_columns]
dataframe_filtered.columns = [col.split('.')[-1] for col in dataframe_filtered.columns]
return dataframe_filtered
else:
return None
# function that extracts the category of the venue
def get_category_type(row):
try:
categories_list = row['categories']
except:
categories_list = row['venue.categories']
if len(categories_list) == 0:
return None
else:
return categories_list[0]['name']
def getNearbyVenues(df_in, radius=500, extend=True):
venues_df = pd.DataFrame(columns=['Postal Code','Venue Name','Venue Latitude','Venue Longitude','Venue Category','Radius'])
for i, row in df_in.iterrows():
# Get the neaby venues
rad = radius
df = FoursquareExplore(row['Latitude'],row['Longitude'],rad)
print(row['Postal Code'])
print(row['Postal Code'], f"radius {rad}", f"entries {df.shape[0] if not df is None else 0}")
if extend:
while (df is None or df.shape[0]<LIMIT/2) and rad<=max(radius,row['NN Distance [m]']/2):
rad = rad+radius/2
df = FoursquareExplore(row['Latitude'],row['Longitude'],rad)
print(row['Postal Code'], f"radius {rad}", f"entries {df.shape[0] if not df is None else 0}")
# Shorten the dataframe
if not df is None:
df_add = pd.DataFrame([[row['Postal Code'],r['name'],r['lat'],r['lng'],get_category_type(r),rad] for _, r in df.iterrows()], columns=venues_df.columns)
venues_df = venues_df.append(df_add,ignore_index=True)
return(venues_df)
def resumableGetNearbyVenues(df_in,df_venues=None,radius=500,extend=True):
# Can be called with partial results in df_venues, and will resume populating it until another quota error from Foursquare
# df_venues is populated only after satisfying query conditions (may need multiple calls if extend=True)
# Need to deal with indexing - .loc uses dataframe indexes which are not necessarily zero-based 1-increment
# TODO: Change selection method to evaluate indices only when they are not found in df_venues, this could also handle change of order
if df_venues is None or df_venues.shape[0]==0:
df_venues = pd.DataFrame(columns=['Postal Code','Venue Name','Venue Latitude','Venue Longitude','Venue Category','Radius'])
current_index = 0
else:
last_postal_code = df_venues['Postal Code'][df_venues.shape[0]-1]
current_index = np.asarray(df_in['Postal Code']==last_postal_code).nonzero()[0][0]+1
# TODO: handle case where a complete df_venues is provided, though this should not occur because isComplete is returned
isComplete = current_index == (df_in.shape[0])
for index in range(current_index,df_in.shape[0]):
try:
df_add = getNearbyVenues(df_in.iloc[[index],:],radius=radius,extend=extend)
df_venues = df_venues.append(df_add, ignore_index=True)
isComplete = index == (df_in.shape[0]-1)
except KeyError:
isComplete = False
print('Error encountered in venue lookup')
break
return isComplete, df_venues
We can now get nearby venues for each postal code. The Radius entry is the radius (in meters) that is required for the Foursquare API to return at least LIMIT/2 (50) venues. Radius is increased in increments of half the input radius until LIMIT/2 venues are returned if the extend parameter is True.
fn_venues = 'df_venues_archived.db'
venues_loaded = False
try:
with open(fn_venues,'rb') as f:
(isComplete, df_venues) = dill.loads(f.read())
venues_loaded = True
print('Venues loaded from file')
except (FileNotFoundError, IOError) as e:
print(f'ERROR on file read: {e}')
print('Polling new venue list...')
isComplete, df_venues = resumableGetNearbyVenues(df_final, None, radius=RADIUS, extend=True)
print('Venue lookup completed successfully' if isComplete else 'Venue lookup interrupted, try repeated execution of next cell')
display(df_venues.head())
if not (isComplete and venues_loaded):
path = os.path.dirname(fn_venues)
if path!='':
os.makedirs(path, exist_ok=True)
try:
with open(fn_venues,'wb+') as file:
file.write(dill.dumps((isComplete, df_venues)))
print('Venue saving completed successfully')
except IOError as e:
print(f'ERROR on file write: {e}')
Venues loaded from file
| Postal Code | Venue Name | Venue Latitude | Venue Longitude | Venue Category | Radius | Category Parents | Category Depth | |
|---|---|---|---|---|---|---|---|---|
| 0 | M3A | Allwyn's Bakery | 43.759840 | -79.324719 | Caribbean Restaurant | 1250.0 | [Food] | 1 |
| 1 | M3A | Brookbanks Park | 43.751976 | -79.332140 | Park | 1250.0 | [Outdoors & Recreation] | 1 |
| 2 | M3A | Bruno's valu-mart | 43.746143 | -79.324630 | Grocery Store | 1250.0 | [Shop & Service, Food & Drink Shop] | 2 |
| 3 | M3A | Tim Hortons | 43.760668 | -79.326368 | Café | 1250.0 | [Food] | 1 |
| 4 | M3A | Shoppers Drug Mart | 43.745315 | -79.325800 | Pharmacy | 1250.0 | [Shop & Service] | 1 |
If the lookup fails due to quotas (or presumably timeout), the following cell can be run multiple times until successful completion (e.g. on multiple days should operation be interrupted due to exceeding quotas).
isComplete=False
if not isComplete:
isComplete, df_venues = resumableGetNearbyVenues(df_final, df_venues, radius=RADIUS, extend=True)
print('Venue lookup completed successfully' if isComplete else 'Venue lookup interrupted, execute this cell again after appropriate delay')
path = os.path.dirname(fn_venues)
if path!='':
os.makedirs(path, exist_ok=True)
try:
with open(fn_venues,'wb+') as file:
file.write(dill.dumps((isComplete, df_venues)))
print('Venue saving completed successfully')
except IOError as e:
print(f'ERROR on file write: {e}')
display(df_venues)
else:
print('Venue lookup is already finished')
Venue lookup completed successfully Venue saving completed successfully
| Postal Code | Venue Name | Venue Latitude | Venue Longitude | Venue Category | Radius | Category Parents | Category Depth | |
|---|---|---|---|---|---|---|---|---|
| 0 | M3A | Allwyn's Bakery | 43.759840 | -79.324719 | Caribbean Restaurant | 1250.0 | [Food] | 1 |
| 1 | M3A | Brookbanks Park | 43.751976 | -79.332140 | Park | 1250.0 | [Outdoors & Recreation] | 1 |
| 2 | M3A | Bruno's valu-mart | 43.746143 | -79.324630 | Grocery Store | 1250.0 | [Shop & Service, Food & Drink Shop] | 2 |
| 3 | M3A | Tim Hortons | 43.760668 | -79.326368 | Café | 1250.0 | [Food] | 1 |
| 4 | M3A | Shoppers Drug Mart | 43.745315 | -79.325800 | Pharmacy | 1250.0 | [Shop & Service] | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4064 | M8Z | Appalachia BBQ | 43.624034 | -79.514079 | BBQ Joint | 1250.0 | [Food] | 1 |
| 4065 | M8Z | Light Up The Floor - Your Lifestyle Studio | 43.635114 | -79.523928 | Gym / Fitness Center | 1250.0 | [Outdoors & Recreation, Athletics & Sports] | 2 |
| 4066 | M8Z | Cora's | 43.617940 | -79.538750 | Breakfast Spot | 1250.0 | [Food] | 1 |
| 4067 | M8Z | Goodwill | 43.621602 | -79.513198 | Thrift / Vintage Store | 1250.0 | [Shop & Service] | 1 |
| 4068 | M8Z | Performance Improvements | 43.635205 | -79.521983 | Automotive Shop | 1250.0 | [Shop & Service] | 1 |
4069 rows × 8 columns
Let's make sure we have some features for every location:
missing_postal_codes = list(np.setdiff1d(df_final['Postal Code'],df_venues['Postal Code']))
print(f"There are {len(missing_postal_codes)} postal codes with no venues: {missing_postal_codes}")
print(f"There are {len(df_venues['Venue Category'].unique())} unique categories among {df_venues.shape[0]} venues (ratio of 1 to {df_venues.shape[0]/len(df_venues['Venue Category'].unique()):.1f}).")
There are 0 postal codes with no venues: [] There are 334 unique categories among 4069 venues (ratio of 1 to 12.2).
Let's see the distribution of number of venues found in each postal code:
df_venue_counts = df_venues[['Postal Code','Venue Category']].groupby('Postal Code').count()
df_venue_counts.hist(bins=np.arange(0,110,2))
plt.xlabel('Number of venues found in a postal code')
plt.ylabel('Count of postal codes');
And the distribution of frequency of venue type across all of Toronto:
df_category_counts = df_venues[['Postal Code','Venue Category']].groupby('Venue Category').count()
df_category_counts.rename(columns={'Postal Code':'Unique Categories'},inplace=True)
df_category_counts.hist(bins=np.arange(0,350,5),log=True)
plt.xlabel('Number of venues in a venue category')
plt.ylabel('Count of venue categories');
Now it seems there is a heirarchy of categories and we might do well to restrict ourselves to higher level categories. Let's get the category data structure:
try:
categories_list = requests.get(f"https://api.foursquare.com/v2/venues/categories?&client_id={CLIENT_ID}&client_secret={CLIENT_SECRET}&v={VERSION}").json()
categories_list = categories_list['response']['categories']
print('Categories retrieved')
except KeyError:
print('There was an error getting categories from Foursquare')
Categories retrieved
def findParentCategories(categories_list,categories_dict={},parents=[]):
# Recursively explore categories_list, and modify categories_dict, where the keys are the categroies and values are lists of parents, highest first.
for c in categories_list:
categories_dict[c['name']] = parents
if len(c['categories'])==0: # Leaf condition
categories_dict[c['name']] = parents
else: # Branch condition
findParentCategories(c['categories'],categories_dict,[*parents, c['name']])
def getCategoriesByDepth(categories_dict,depth=0):
# Returns a dict of all categories at the given depth (with parent list for values)
ret = {}
for key, val in categories_dict.items():
if len(val)==depth:
ret[key] = val
return ret
def getCategoryDepth(categories_dict,category):
# Returns the integer dpeth of category in categories_dict
return len(categories_dict['category'])
categories_dict = {}
findParentCategories(categories_list,categories_dict)
print(f"There are {len(categories_dict)} unique category labels.")
There are 971 unique category labels.
And we can examine how many labels are at each level of the hierarchy:
category_count = []
categories_depth_list = []
while sum(category_count)<len(categories_dict):
categories_depth_list.append(getCategoriesByDepth(categories_dict,len(category_count)))
category_count.append(len(categories_depth_list[-1]))
print(f"There are {category_count[-1]} categories at depth {len(category_count)-1}.")
There are 10 categories at depth 0. There are 470 categories at depth 1. There are 383 categories at depth 2. There are 94 categories at depth 3. There are 14 categories at depth 4.
This can quickly find the sum of branch and leaf categories at each depth in our data:
df_venues['Category Parents'] = [categories_dict[row['Venue Category']] for _, row in df_venues.iterrows()]
df_venues['Category Depth'] = df_venues['Category Parents'].apply(len)
display(df_venues.head(3))
for k in np.sort(df_venues['Category Depth'].unique()):
colname = f"Venue Category, Depth {k}"
print(f"At depth {k} there are {sum(df_venues['Category Depth']==k)} venue category entries.")
| Postal Code | Venue Name | Venue Latitude | Venue Longitude | Venue Category | Radius | Category Parents | Category Depth | |
|---|---|---|---|---|---|---|---|---|
| 0 | M3A | Allwyn's Bakery | 43.759840 | -79.324719 | Caribbean Restaurant | 1250.0 | [Food] | 1 |
| 1 | M3A | Brookbanks Park | 43.751976 | -79.332140 | Park | 1250.0 | [Outdoors & Recreation] | 1 |
| 2 | M3A | Bruno's valu-mart | 43.746143 | -79.324630 | Grocery Store | 1250.0 | [Shop & Service, Food & Drink Shop] | 2 |
At depth 0 there are 2 venue category entries. At depth 1 there are 3042 venue category entries. At depth 2 there are 864 venue category entries. At depth 3 there are 160 venue category entries. At depth 4 there are 1 venue category entries.
Alternatively, we can examine explicitly how many labels are leaf or branch nodes for all potential categories:
leaf_list = []
node_list = []
for k in range(len(categories_depth_list)):
leaf_list.append([])
node_list.append([])
for key, val in categories_dict.items(): # Go through the dict; if a key appears in a value, it is a branch, if it never appears in a value (as a parent), it is a leaf
node = False
for k, v in categories_dict.items():
if key==k:
pass
elif key in v: # Key appears as a parent
node_list[len(val)].append(key)
node=True
break
if not node: # Key did not appear as a parent - it is a leaf
leaf_list[len(val)].append(key)
for k in range(len(leaf_list)):
print(f"At depth {k} there are {len(node_list[k])} branches and {len(leaf_list[k])} leaves.")
At depth 0 there are 10 branches and 0 leaves. At depth 1 there are 53 branches and 417 leaves. At depth 2 there are 10 branches and 373 leaves. At depth 3 there are 2 branches and 92 leaves. At depth 4 there are 0 branches and 14 leaves.
And then discover whether the Toronto venue data categories occur at branch or leaf nodes:
leaf_toronto = [[] for k in range(len(leaf_list))]
node_toronto = [[] for k in range(len(node_list))]
for cat in df_venues['Venue Category']:
for k in range(len(leaf_list)):
if cat in leaf_list[k]:
leaf_toronto[k].append(cat)
if cat in node_list[k]:
node_toronto[k].append(cat)
for k in range(len(leaf_toronto)):
print(f"At depth {k} there are {len(node_toronto[k])} categories that are branches and {len(leaf_toronto[k])} categories that are leaves.")
At depth 0 there are 2 categories that are branches and 0 categories that are leaves. At depth 1 there are 633 categories that are branches and 2409 categories that are leaves. At depth 2 there are 225 categories that are branches and 639 categories that are leaves. At depth 3 there are 2 categories that are branches and 158 categories that are leaves. At depth 4 there are 0 categories that are branches and 1 categories that are leaves.
We can create one-hots both for leaf and node categories and to assign more weight in the clustering to lower depth nodes (to emphasize matching broader categories first, but this may get needlessly complex. We can generate the additional one-hot dataframes just in case, but then continue with the simpler analysis first.
Note that this could be done by limiting different parent categories to different depths of children, but that is too complex for now.
Note also that it would be reasonable to compact some venues (like zoo exhibits) into one entry in order to not overemphasize certain venues (like zoos), but this is also a bit beyond the current complexity level.
df_venues.head()
| Postal Code | Venue Name | Venue Latitude | Venue Longitude | Venue Category | Radius | Category Parents | Category Depth | |
|---|---|---|---|---|---|---|---|---|
| 0 | M3A | Allwyn's Bakery | 43.759840 | -79.324719 | Caribbean Restaurant | 1250.0 | [Food] | 1 |
| 1 | M3A | Brookbanks Park | 43.751976 | -79.332140 | Park | 1250.0 | [Outdoors & Recreation] | 1 |
| 2 | M3A | Bruno's valu-mart | 43.746143 | -79.324630 | Grocery Store | 1250.0 | [Shop & Service, Food & Drink Shop] | 2 |
| 3 | M3A | Tim Hortons | 43.760668 | -79.326368 | Café | 1250.0 | [Food] | 1 |
| 4 | M3A | Shoppers Drug Mart | 43.745315 | -79.325800 | Pharmacy | 1250.0 | [Shop & Service] | 1 |
for k in range(df_venues['Category Depth'].max()+1):
df_venues[f"Venue Category, Depth {k}"] = [row['Venue Category'] if row['Category Depth']<=k else row['Category Parents'][k] for _, row in df_venues.iterrows()]
df_venues.head()
| Postal Code | Venue Name | Venue Latitude | Venue Longitude | Venue Category | Radius | Category Parents | Category Depth | Venue Category, Depth 0 | Venue Category, Depth 1 | Venue Category, Depth 2 | Venue Category, Depth 3 | Venue Category, Depth 4 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | Allwyn's Bakery | 43.759840 | -79.324719 | Caribbean Restaurant | 1250.0 | [Food] | 1 | Food | Caribbean Restaurant | Caribbean Restaurant | Caribbean Restaurant | Caribbean Restaurant |
| 1 | M3A | Brookbanks Park | 43.751976 | -79.332140 | Park | 1250.0 | [Outdoors & Recreation] | 1 | Outdoors & Recreation | Park | Park | Park | Park |
| 2 | M3A | Bruno's valu-mart | 43.746143 | -79.324630 | Grocery Store | 1250.0 | [Shop & Service, Food & Drink Shop] | 2 | Shop & Service | Food & Drink Shop | Grocery Store | Grocery Store | Grocery Store |
| 3 | M3A | Tim Hortons | 43.760668 | -79.326368 | Café | 1250.0 | [Food] | 1 | Food | Café | Café | Café | Café |
| 4 | M3A | Shoppers Drug Mart | 43.745315 | -79.325800 | Pharmacy | 1250.0 | [Shop & Service] | 1 | Shop & Service | Pharmacy | Pharmacy | Pharmacy | Pharmacy |
It may be useful to know how many categories we have to deal with at each depth:
for k in np.sort(df_venues['Category Depth'].unique()):
colname = f"Venue Category, Depth {k}"
print(f"There are {len(df_venues[colname].unique())} unique categories limited to depth {k}")
There are 9 unique categories limited to depth 0 There are 224 unique categories limited to depth 1 There are 311 unique categories limited to depth 2 There are 333 unique categories limited to depth 3 There are 334 unique categories limited to depth 4
From all this we expect that most the optimal category depth will be 1 or 2; depth 0 is quite general, while depth above 2 becomes too specific and adds little additional information (no information in the case of depth 4).
Note that the columns 'Venue Category' and 'Venue Category, Depth 4' are equivalent.
all(df_venues['Venue Category']==df_venues['Venue Category, Depth 4'])
True
def makeOneHot(df,colname,keycolname):
# Returns (onehot of df[colname] with first column df[keycolname],
# df with leading column df[keycolname].unique() and rows the normalized venue frequency vector and columns the venue category
onehot = pd.get_dummies(df[[colname]], prefix="", prefix_sep="")
onehot[keycolname] = df[keycolname]
onehot = onehot[[onehot.columns[-1]] + list(onehot.columns[:-1])]
grouped = onehot.groupby(keycolname).mean().reset_index()
return onehot, grouped
# Basic one-hot encoding
toronto_onehot, toronto_grouped = makeOneHot(df_venues,'Venue Category','Postal Code')
# Create columns to hold venues limited to a given depth
toronto_onehot_0, toronto_grouped_0 = makeOneHot(df_venues,'Venue Category, Depth 0','Postal Code')
toronto_onehot_1, toronto_grouped_1 = makeOneHot(df_venues,'Venue Category, Depth 1','Postal Code')
toronto_onehot_2, toronto_grouped_2 = makeOneHot(df_venues,'Venue Category, Depth 2','Postal Code')
toronto_onehot_3, toronto_grouped_3 = makeOneHot(df_venues,'Venue Category, Depth 3','Postal Code')
toronto_onehot_4, toronto_grouped_4 = makeOneHot(df_venues,'Venue Category, Depth 4','Postal Code')
print()
print('Full Depth category coding:')
print()
display(toronto_onehot.head())
print(f"Shape of Full Depth category coding: {toronto_onehot.shape}")
print()
display(toronto_grouped.head())
print(f"Shape of Full Depth category coding, grouped by Postal Code: {toronto_grouped.shape}")
print()
print()
print('Depth 0 category coding:')
print()
display(toronto_onehot_0.head())
print(f"Shape of Depth 0 category coding: {toronto_onehot_0.shape}")
print()
display(toronto_grouped_0.head())
print(f"Shape of Depth 0 category coding, grouped by Postal Code: {toronto_grouped_0.shape}")
Full Depth category coding:
| Postal Code | ATM | Adult Boutique | Afghan Restaurant | African Restaurant | Airport | American Restaurant | Animal Shelter | Antique Shop | Aquarium | ... | Vietnamese Restaurant | Volleyball Court | Warehouse Store | Waste Facility | Wine Bar | Wine Shop | Wings Joint | Women's Store | Yoga Studio | Zoo Exhibit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | M3A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | M3A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | M3A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | M3A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 335 columns
Shape of Full Depth category coding: (4069, 335)
| Postal Code | ATM | Adult Boutique | Afghan Restaurant | African Restaurant | Airport | American Restaurant | Animal Shelter | Antique Shop | Aquarium | ... | Vietnamese Restaurant | Volleyball Court | Warehouse Store | Waste Facility | Wine Bar | Wine Shop | Wings Joint | Women's Store | Yoga Studio | Zoo Exhibit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M1B | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.451613 |
| 1 | M1C | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| 2 | M1E | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| 3 | M1G | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| 4 | M1H | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
5 rows × 335 columns
Shape of Full Depth category coding, grouped by Postal Code: (96, 335) Depth 0 category coding:
| Postal Code | Arts & Entertainment | College & University | Food | Nightlife Spot | Outdoors & Recreation | Professional & Other Places | Residence | Shop & Service | Travel & Transport | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M3A | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | M3A | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 2 | M3A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | M3A | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | M3A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Shape of Depth 0 category coding: (4069, 10)
| Postal Code | Arts & Entertainment | College & University | Food | Nightlife Spot | Outdoors & Recreation | Professional & Other Places | Residence | Shop & Service | Travel & Transport | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M1B | 0.451613 | 0.0 | 0.193548 | 0.000000 | 0.161290 | 0.000000 | 0.0 | 0.193548 | 0.000000 |
| 1 | M1C | 0.000000 | 0.0 | 0.384615 | 0.000000 | 0.461538 | 0.000000 | 0.0 | 0.153846 | 0.000000 |
| 2 | M1E | 0.000000 | 0.0 | 0.448980 | 0.040816 | 0.122449 | 0.020408 | 0.0 | 0.306122 | 0.061224 |
| 3 | M1G | 0.000000 | 0.0 | 0.555556 | 0.000000 | 0.222222 | 0.000000 | 0.0 | 0.222222 | 0.000000 |
| 4 | M1H | 0.000000 | 0.0 | 0.652174 | 0.000000 | 0.086957 | 0.000000 | 0.0 | 0.217391 | 0.043478 |
Shape of Depth 0 category coding, grouped by Postal Code: (96, 10)
It may be interesting to examine the venues occurring at highest frequency in each postal code. We do so below, but note that this is not used in feature generation in favor of the grouped onehot vectors. Using features created only from the highest frequency venue categories would be an interesting approach to try (some other time).
def printNeighborhoodVenueFrequency(df_tmp, key_col_name='Postal Code', num_top_venues=5):
for hood in df_tmp[key_col_name]:
neighborhoods_str = ''
for n in df_final.loc[df_final['Postal Code']==hood,'Neighbourhood']:
neighborhoods_str += n
print("----"+hood+": "+neighborhoods_str+"----")
temp = df_tmp[df_tmp[key_col_name] == hood].T.reset_index()
temp.columns = ['venue','freq']
temp = temp.iloc[1:]
temp['freq'] = temp['freq'].astype(float)
temp = temp.round({'freq': 2})
print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
print('\n')
printNeighborhoodVenueFrequency(toronto_grouped,'Postal Code',5)
----M1B: Malvern, Rouge----
venue freq
0 Zoo Exhibit 0.45
1 Fast Food Restaurant 0.06
2 Trail 0.06
3 Hobby Shop 0.03
4 Restaurant 0.03
----M1C: Rouge Hill, Port Union, Highland Creek----
venue freq
0 Park 0.15
1 Italian Restaurant 0.08
2 Playground 0.08
3 Neighborhood 0.08
4 Breakfast Spot 0.08
----M1E: Guildwood, Morningside, West Hill----
venue freq
0 Pizza Place 0.08
1 Park 0.06
2 Restaurant 0.06
3 Breakfast Spot 0.06
4 Electronics Store 0.04
----M1G: Woburn----
venue freq
0 Park 0.22
1 Coffee Shop 0.22
2 Pharmacy 0.11
3 Juice Bar 0.11
4 Fast Food Restaurant 0.11
----M1H: Cedarbrae----
venue freq
0 Indian Restaurant 0.17
1 Coffee Shop 0.09
2 Burger Joint 0.04
3 Playground 0.04
4 Bus Line 0.04
----M1J: Scarborough Village----
venue freq
0 Coffee Shop 0.19
1 Ice Cream Shop 0.12
2 Sandwich Place 0.12
3 Playground 0.06
4 Pizza Place 0.06
----M1K: Kennedy Park, Ionview, East Birchmount Park----
venue freq
0 Discount Store 0.12
1 Chinese Restaurant 0.09
2 Fast Food Restaurant 0.09
3 Sandwich Place 0.06
4 Bus Line 0.06
----M1L: Golden Mile, Clairlea, Oakridge----
venue freq
0 Coffee Shop 0.07
1 Grocery Store 0.07
2 Trail 0.03
3 Hardware Store 0.03
4 Mexican Restaurant 0.03
----M1M: Cliffside, Cliffcrest, Scarborough Village West----
venue freq
0 Bistro 0.11
1 Park 0.11
2 Fast Food Restaurant 0.11
3 Ice Cream Shop 0.11
4 Sporting Goods Shop 0.11
----M1N: Birch Cliff, Cliffside West----
venue freq
0 Coffee Shop 0.10
1 Pizza Place 0.10
2 Sandwich Place 0.05
3 Café 0.05
4 General Entertainment 0.05
----M1P: Dorset Park, Wexford Heights, Scarborough Town Centre----
venue freq
0 Coffee Shop 0.10
1 Fast Food Restaurant 0.08
2 Pizza Place 0.06
3 Furniture / Home Store 0.06
4 Park 0.06
----M1R: Wexford, Maryvale----
venue freq
0 Middle Eastern Restaurant 0.08
1 Pizza Place 0.06
2 Coffee Shop 0.06
3 Grocery Store 0.04
4 Sandwich Place 0.04
----M1S: Agincourt----
venue freq
0 Chinese Restaurant 0.16
1 Coffee Shop 0.07
2 Park 0.05
3 Shopping Mall 0.05
4 Caribbean Restaurant 0.04
----M1T: Clarks Corners, Tam O'Shanter, Sullivan----
venue freq
0 Fast Food Restaurant 0.10
1 Chinese Restaurant 0.08
2 Pharmacy 0.08
3 Coffee Shop 0.08
4 Bank 0.05
----M1V: Milliken, Agincourt North, Steeles East, L'Amoreaux East----
venue freq
0 Chinese Restaurant 0.17
1 Coffee Shop 0.07
2 Pizza Place 0.05
3 Noodle House 0.03
4 Dessert Shop 0.03
----M1W: Steeles West, L'Amoreaux West----
venue freq
0 Chinese Restaurant 0.12
1 Fast Food Restaurant 0.07
2 Coffee Shop 0.07
3 Intersection 0.07
4 Pizza Place 0.05
----M1X: Upper Rouge----
venue freq
0 Park 0.33
1 Golf Course 0.33
2 Playground 0.17
3 Intersection 0.17
4 Pub 0.00
----M2H: Hillcrest Village----
venue freq
0 Coffee Shop 0.14
1 Sandwich Place 0.09
2 Bank 0.09
3 Pharmacy 0.09
4 Park 0.07
----M2J: Fairview, Henry Farm, Oriole----
venue freq
0 Clothing Store 0.22
1 Coffee Shop 0.06
2 Fast Food Restaurant 0.06
3 Restaurant 0.06
4 Juice Bar 0.04
----M2K: Bayview Village----
venue freq
0 Café 0.08
1 Bank 0.06
2 Park 0.06
3 Coffee Shop 0.06
4 Clothing Store 0.04
----M2L: York Mills, Silver Hills----
venue freq
0 Café 0.2
1 Recreation Center 0.2
2 Gym / Fitness Center 0.2
3 Carpet Store 0.2
4 General Entertainment 0.2
----M2M: Willowdale, Newtonbrook----
venue freq
0 Coffee Shop 0.13
1 Korean Restaurant 0.11
2 Café 0.07
3 Middle Eastern Restaurant 0.07
4 Restaurant 0.06
----M2N: Willowdale, Willowdale East----
venue freq
0 Korean Restaurant 0.07
1 Fast Food Restaurant 0.07
2 Japanese Restaurant 0.05
3 Pizza Place 0.05
4 Coffee Shop 0.05
----M2P: York Mills West----
venue freq
0 Coffee Shop 0.18
1 Park 0.12
2 Restaurant 0.12
3 Business Service 0.06
4 Thai Restaurant 0.06
----M2R: Willowdale, Willowdale West----
venue freq
0 Park 0.15
1 Coffee Shop 0.15
2 Pizza Place 0.15
3 Grocery Store 0.10
4 Intersection 0.05
----M3A: Parkwoods----
venue freq
0 Coffee Shop 0.11
1 Intersection 0.06
2 Pharmacy 0.06
3 Bus Stop 0.06
4 Park 0.06
----M3B: Don Mills----
venue freq
0 Coffee Shop 0.07
1 Japanese Restaurant 0.05
2 Pizza Place 0.05
3 Restaurant 0.04
4 Chinese Restaurant 0.04
----M3C: Don Mills----
venue freq
0 Japanese Restaurant 0.08
1 Gym 0.08
2 Coffee Shop 0.08
3 Restaurant 0.05
4 Sandwich Place 0.05
----M3H: Bathurst Manor, Wilson Heights, Downsview North----
venue freq
0 Pizza Place 0.08
1 Coffee Shop 0.08
2 Park 0.06
3 Bank 0.06
4 Gas Station 0.06
----M3J: Northwood Park, York University----
venue freq
0 Coffee Shop 0.23
1 Pizza Place 0.08
2 Chinese Restaurant 0.05
3 Restaurant 0.05
4 Sandwich Place 0.05
----M3K: Downsview----
venue freq
0 Coffee Shop 0.12
1 Park 0.12
2 Turkish Restaurant 0.12
3 Vietnamese Restaurant 0.06
4 Gas Station 0.06
----M3L: Downsview----
venue freq
0 Moving Target 0.14
1 Vietnamese Restaurant 0.14
2 Park 0.14
3 Shopping Mall 0.14
4 Coffee Shop 0.14
----M3M: Downsview----
venue freq
0 Park 0.33
1 Food Truck 0.33
2 Mobile Phone Shop 0.33
3 Optical Shop 0.00
4 Pakistani Restaurant 0.00
----M3N: Downsview----
venue freq
0 Hotel 0.13
1 Pizza Place 0.07
2 Discount Store 0.07
3 Theater 0.07
4 Coffee Shop 0.07
----M4A: Victoria Village----
venue freq
0 Coffee Shop 0.16
1 Thrift / Vintage Store 0.06
2 Pizza Place 0.06
3 Bookstore 0.06
4 Gym 0.03
----M4B: Parkview Hill, Woodbine Gardens----
venue freq
0 Pizza Place 0.10
1 Coffee Shop 0.10
2 Bakery 0.10
3 Brewery 0.10
4 Athletics & Sports 0.05
----M4C: Woodbine Heights----
venue freq
0 Skating Rink 0.07
1 Playground 0.05
2 Coffee Shop 0.05
3 Pizza Place 0.05
4 Park 0.05
----M4E: The Beaches----
venue freq
0 Pub 0.09
1 Coffee Shop 0.08
2 Pizza Place 0.03
3 Bakery 0.03
4 Park 0.03
----M4G: Leaside----
venue freq
0 Coffee Shop 0.07
1 Sporting Goods Shop 0.06
2 Sushi Restaurant 0.04
3 Electronics Store 0.04
4 Furniture / Home Store 0.03
----M4H: Thorncliffe Park----
venue freq
0 Indian Restaurant 0.08
1 Afghan Restaurant 0.05
2 Restaurant 0.05
3 Grocery Store 0.05
4 Turkish Restaurant 0.05
----M4J: East Toronto, Broadview North (Old East York)----
venue freq
0 Coffee Shop 0.10
1 Café 0.07
2 Ethiopian Restaurant 0.05
3 Sandwich Place 0.05
4 Park 0.05
----M4K: The Danforth West, Riverdale----
venue freq
0 Greek Restaurant 0.10
1 Café 0.05
2 Coffee Shop 0.05
3 Pub 0.04
4 Pizza Place 0.03
----M4L: India Bazaar, The Beaches West----
venue freq
0 Indian Restaurant 0.06
1 Coffee Shop 0.05
2 Grocery Store 0.04
3 Beach 0.04
4 Pub 0.04
----M4M: Studio District----
venue freq
0 Coffee Shop 0.07
1 Bar 0.04
2 Café 0.04
3 Diner 0.04
4 Pizza Place 0.03
----M4N: Lawrence Park----
venue freq
0 Restaurant 0.11
1 Café 0.11
2 Gym / Fitness Center 0.11
3 Trail 0.11
4 Coffee Shop 0.11
----M4P: Davisville North----
venue freq
0 Park 0.11
1 Pizza Place 0.11
2 Café 0.06
3 Greek Restaurant 0.06
4 Food & Drink Shop 0.06
----M4R: North Toronto West, Lawrence Park----
venue freq
0 Sporting Goods Shop 0.10
1 Coffee Shop 0.08
2 Café 0.08
3 Restaurant 0.05
4 Diner 0.05
----M4S: Davisville----
venue freq
0 Pizza Place 0.07
1 Sandwich Place 0.07
2 Café 0.07
3 Dessert Shop 0.07
4 Coffee Shop 0.07
----M4T: Moore Park, Summerhill East----
venue freq
0 Park 0.25
1 Café 0.12
2 Grocery Store 0.12
3 Thai Restaurant 0.12
4 Sandwich Place 0.12
----M4V: Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park----
venue freq
0 Coffee Shop 0.13
1 Italian Restaurant 0.08
2 Sushi Restaurant 0.06
3 Restaurant 0.04
4 Pizza Place 0.04
----M4W: Rosedale----
venue freq
0 Park 0.33
1 Trail 0.17
2 Grocery Store 0.17
3 Candy Store 0.17
4 Playground 0.17
----M4X: St. James Town, Cabbagetown----
venue freq
0 Restaurant 0.07
1 Park 0.07
2 Coffee Shop 0.05
3 Café 0.05
4 Pizza Place 0.05
----M4Y: Church and Wellesley----
venue freq
0 Coffee Shop 0.11
1 Japanese Restaurant 0.06
2 Sushi Restaurant 0.05
3 Restaurant 0.05
4 Gay Bar 0.04
----M5A: Regent Park, Harbourfront----
venue freq
0 Coffee Shop 0.15
1 Restaurant 0.06
2 Café 0.06
3 Park 0.04
4 Bakery 0.03
----M5B: Garden District, Ryerson----
venue freq
0 Coffee Shop 0.10
1 Clothing Store 0.06
2 Café 0.04
3 Middle Eastern Restaurant 0.03
4 Hotel 0.03
----M5C: St. James Town----
venue freq
0 Coffee Shop 0.08
1 Café 0.05
2 Restaurant 0.04
3 Cosmetics Shop 0.04
4 Bakery 0.03
----M5E: Berczy Park----
venue freq
0 Coffee Shop 0.08
1 Bakery 0.05
2 Seafood Restaurant 0.05
3 Cocktail Bar 0.03
4 Hotel 0.03
----M5G: Central Bay Street----
venue freq
0 Coffee Shop 0.12
1 Clothing Store 0.09
2 Plaza 0.04
3 Hotel 0.04
4 Cosmetics Shop 0.03
----M5H: Richmond, Adelaide, King----
venue freq
0 Café 0.06
1 Coffee Shop 0.06
2 Gym 0.04
3 Asian Restaurant 0.03
4 Restaurant 0.03
----M5J: Harbourfront East, Union Station, Toronto Islands----
venue freq
0 Coffee Shop 0.13
1 Hotel 0.07
2 Restaurant 0.05
3 Japanese Restaurant 0.05
4 Park 0.03
----M5M: Bedford Park, Lawrence Manor East----
venue freq
0 Coffee Shop 0.08
1 Pizza Place 0.05
2 Fast Food Restaurant 0.05
3 Pharmacy 0.05
4 Sandwich Place 0.05
----M5N: Roselawn----
venue freq
0 Playground 0.75
1 Garden 0.25
2 ATM 0.00
3 Opera House 0.00
4 Paintball Field 0.00
----M5P: Forest Hill North & West, Forest Hill Road Park----
venue freq
0 Bank 0.08
1 Café 0.08
2 Japanese Restaurant 0.04
3 Grocery Store 0.04
4 Trail 0.04
----M5R: The Annex, North Midtown, Yorkville----
venue freq
0 Café 0.09
1 Italian Restaurant 0.07
2 Sandwich Place 0.07
3 Coffee Shop 0.07
4 History Museum 0.05
----M5S: University of Toronto, Harbord----
venue freq
0 Café 0.09
1 Coffee Shop 0.07
2 Bakery 0.06
3 Bar 0.04
4 Gym 0.04
----M5T: Kensington Market, Chinatown, Grange Park----
venue freq
0 Café 0.08
1 Bar 0.08
2 Dumpling Restaurant 0.05
3 Vietnamese Restaurant 0.05
4 Vegetarian / Vegan Restaurant 0.05
----M5V: CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport----
venue freq
0 Italian Restaurant 0.08
1 Coffee Shop 0.06
2 Café 0.05
3 Park 0.04
4 Bar 0.04
----M6A: Lawrence Manor, Lawrence Heights----
venue freq
0 Clothing Store 0.19
1 Furniture / Home Store 0.06
2 Women's Store 0.04
3 Sporting Goods Shop 0.04
4 Food Court 0.04
----M6B: Glencairn----
venue freq
0 Grocery Store 0.11
1 Park 0.07
2 Gas Station 0.07
3 Fast Food Restaurant 0.04
4 Pizza Place 0.04
----M6C: Humewood-Cedarvale----
venue freq
0 Convenience Store 0.25
1 Hockey Arena 0.12
2 Trail 0.12
3 Deli / Bodega 0.12
4 Grocery Store 0.12
----M6E: Caledonia-Fairbanks----
venue freq
0 Park 0.08
1 Bus Stop 0.08
2 Cosmetics Shop 0.04
3 Gym 0.04
4 Coffee Shop 0.04
----M6G: Christie----
venue freq
0 Korean Restaurant 0.13
1 Grocery Store 0.10
2 Coffee Shop 0.06
3 Café 0.05
4 Park 0.04
----M6H: Dufferin, Dovercourt Village----
venue freq
0 Bar 0.09
1 Café 0.08
2 Coffee Shop 0.08
3 Bakery 0.06
4 Mexican Restaurant 0.05
----M6J: Little Portugal, Trinity----
venue freq
0 Bar 0.07
1 Café 0.06
2 Restaurant 0.05
3 Italian Restaurant 0.05
4 Vegetarian / Vegan Restaurant 0.04
----M6K: Brockton, Parkdale Village, Exhibition Place----
venue freq
0 Coffee Shop 0.10
1 Restaurant 0.07
2 Bar 0.06
3 Furniture / Home Store 0.06
4 Gym 0.04
----M6L: North Park, Maple Leaf Park, Upwood Park----
venue freq
0 Coffee Shop 0.14
1 Supermarket 0.09
2 Pizza Place 0.09
3 Vietnamese Restaurant 0.09
4 Sandwich Place 0.05
----M6M: Del Ray, Mount Dennis, Keelsdale and Silverthorn----
venue freq
0 Furniture / Home Store 0.19
1 Coffee Shop 0.12
2 Fast Food Restaurant 0.12
3 Gas Station 0.12
4 Gym / Fitness Center 0.06
----M6N: Runnymede, The Junction North----
venue freq
0 Coffee Shop 0.08
1 Sandwich Place 0.08
2 Gas Station 0.06
3 Café 0.06
4 Beer Store 0.04
----M6P: High Park, The Junction South----
venue freq
0 Convenience Store 0.10
1 Bar 0.08
2 Coffee Shop 0.06
3 Pizza Place 0.06
4 Thai Restaurant 0.06
----M6R: Parkdale, Roncesvalles----
venue freq
0 Coffee Shop 0.07
1 Eastern European Restaurant 0.05
2 Café 0.05
3 Thai Restaurant 0.05
4 Sushi Restaurant 0.05
----M6S: Runnymede, Swansea----
venue freq
0 Coffee Shop 0.08
1 Café 0.08
2 Pizza Place 0.05
3 Bakery 0.05
4 Pub 0.03
----M8V: New Toronto, Mimico South, Humber Bay Shores----
venue freq
0 Park 0.11
1 Indian Restaurant 0.08
2 Bakery 0.06
3 Italian Restaurant 0.06
4 Pizza Place 0.06
----M8W: Alderwood, Long Branch----
venue freq
0 Park 0.09
1 Coffee Shop 0.09
2 Pizza Place 0.09
3 Grocery Store 0.06
4 Bank 0.06
----M8X: The Kingsway, Montgomery Road, Old Mill North----
venue freq
0 Coffee Shop 0.09
1 Breakfast Spot 0.04
2 Pizza Place 0.04
3 Italian Restaurant 0.04
4 Sushi Restaurant 0.04
----M8Y: Old Mill South, King's Mill Park, Sunnylea, Humber Bay, Mimico NE, The Queensway East, Royal York South East, Kingsway Park South East----
venue freq
0 Italian Restaurant 0.11
1 Coffee Shop 0.11
2 Park 0.08
3 Grocery Store 0.05
4 Ice Cream Shop 0.05
----M8Z: Mimico NW, The Queensway West, South of Bloor, Kingsway Park South West, Royal York South West----
venue freq
0 Restaurant 0.09
1 Sandwich Place 0.06
2 Coffee Shop 0.06
3 Bank 0.06
4 Burrito Place 0.04
----M9A: Islington Avenue, Humber Valley Village----
venue freq
0 Pharmacy 0.20
1 Shopping Mall 0.13
2 Bank 0.13
3 Supermarket 0.07
4 Café 0.07
----M9B: West Deane Park, Princess Gardens, Martin Grove, Islington, Cloverdale----
venue freq
0 Convenience Store 0.12
1 Hotel 0.12
2 Park 0.12
3 Pizza Place 0.12
4 Mexican Restaurant 0.06
----M9C: Eringate, Bloordale Gardens, Old Burnhamthorpe, Markland Wood----
venue freq
0 Baseball Field 0.09
1 Coffee Shop 0.09
2 Pet Store 0.09
3 Pizza Place 0.09
4 Pharmacy 0.05
----M9L: Humber Summit----
venue freq
0 Asian Restaurant 0.11
1 Fast Food Restaurant 0.11
2 Bank 0.11
3 Sandwich Place 0.11
4 Italian Restaurant 0.05
----M9M: Humberlea, Emery----
venue freq
0 Coffee Shop 0.25
1 Discount Store 0.17
2 Park 0.17
3 Latin American Restaurant 0.08
4 Grocery Store 0.08
----M9N: Weston----
venue freq
0 Fried Chicken Joint 0.11
1 Diner 0.11
2 Grocery Store 0.11
3 Train Station 0.11
4 Pharmacy 0.11
----M9P: Westmount----
venue freq
0 Gas Station 0.12
1 Pizza Place 0.12
2 Discount Store 0.06
3 Flea Market 0.06
4 Sandwich Place 0.06
----M9R: Kingsview Village, St. Phillips, Martin Grove Gardens, Richview Gardens----
venue freq
0 Pharmacy 0.12
1 Pizza Place 0.12
2 Beer Store 0.08
3 Shopping Mall 0.08
4 Bank 0.08
----M9V: South Steeles, Silverstone, Humbergate, Jamestown, Mount Olive, Beaumond Heights, Thistletown, Albion Gardens----
venue freq
0 Grocery Store 0.16
1 Pizza Place 0.16
2 Coffee Shop 0.11
3 Bus Line 0.05
4 Caribbean Restaurant 0.05
----M9W: Northwest, West Humber - Clairville----
venue freq
0 Coffee Shop 0.10
1 Fast Food Restaurant 0.10
2 Asian Restaurant 0.06
3 Department Store 0.06
4 Sandwich Place 0.06
printNeighborhoodVenueFrequency(toronto_grouped_0,'Postal Code',5)
----M1B: Malvern, Rouge----
venue freq
0 Arts & Entertainment 0.45
1 Food 0.19
2 Shop & Service 0.19
3 Outdoors & Recreation 0.16
4 College & University 0.00
----M1C: Rouge Hill, Port Union, Highland Creek----
venue freq
0 Outdoors & Recreation 0.46
1 Food 0.38
2 Shop & Service 0.15
3 Arts & Entertainment 0.00
4 College & University 0.00
----M1E: Guildwood, Morningside, West Hill----
venue freq
0 Food 0.45
1 Shop & Service 0.31
2 Outdoors & Recreation 0.12
3 Travel & Transport 0.06
4 Nightlife Spot 0.04
----M1G: Woburn----
venue freq
0 Food 0.56
1 Outdoors & Recreation 0.22
2 Shop & Service 0.22
3 Arts & Entertainment 0.00
4 College & University 0.00
----M1H: Cedarbrae----
venue freq
0 Food 0.65
1 Shop & Service 0.22
2 Outdoors & Recreation 0.09
3 Travel & Transport 0.04
4 Arts & Entertainment 0.00
----M1J: Scarborough Village----
venue freq
0 Food 0.62
1 Shop & Service 0.25
2 Outdoors & Recreation 0.12
3 Arts & Entertainment 0.00
4 College & University 0.00
----M1K: Kennedy Park, Ionview, East Birchmount Park----
venue freq
0 Food 0.38
1 Shop & Service 0.34
2 Travel & Transport 0.22
3 Arts & Entertainment 0.03
4 Outdoors & Recreation 0.03
----M1L: Golden Mile, Clairlea, Oakridge----
venue freq
0 Food 0.40
1 Shop & Service 0.40
2 Travel & Transport 0.10
3 Arts & Entertainment 0.03
4 Nightlife Spot 0.03
----M1M: Cliffside, Cliffcrest, Scarborough Village West----
venue freq
0 Food 0.67
1 Shop & Service 0.22
2 Outdoors & Recreation 0.11
3 Arts & Entertainment 0.00
4 College & University 0.00
----M1N: Birch Cliff, Cliffside West----
venue freq
0 Food 0.65
1 Outdoors & Recreation 0.15
2 Arts & Entertainment 0.05
3 College & University 0.05
4 Nightlife Spot 0.05
----M1P: Dorset Park, Wexford Heights, Scarborough Town Centre----
venue freq
0 Food 0.51
1 Shop & Service 0.25
2 Outdoors & Recreation 0.12
3 Travel & Transport 0.10
4 Nightlife Spot 0.02
----M1R: Wexford, Maryvale----
venue freq
0 Food 0.59
1 Shop & Service 0.24
2 Travel & Transport 0.08
3 Arts & Entertainment 0.02
4 Nightlife Spot 0.02
----M1S: Agincourt----
venue freq
0 Food 0.70
1 Shop & Service 0.16
2 Outdoors & Recreation 0.11
3 Arts & Entertainment 0.02
4 Nightlife Spot 0.02
----M1T: Clarks Corners, Tam O'Shanter, Sullivan----
venue freq
0 Food 0.68
1 Shop & Service 0.25
2 Travel & Transport 0.05
3 Nightlife Spot 0.02
4 Arts & Entertainment 0.00
----M1V: Milliken, Agincourt North, Steeles East, L'Amoreaux East----
venue freq
0 Food 0.75
1 Shop & Service 0.13
2 Outdoors & Recreation 0.07
3 Arts & Entertainment 0.02
4 Professional & Other Places 0.02
----M1W: Steeles West, L'Amoreaux West----
venue freq
0 Food 0.54
1 Outdoors & Recreation 0.17
2 Shop & Service 0.17
3 Travel & Transport 0.10
4 Arts & Entertainment 0.02
----M1X: Upper Rouge----
venue freq
0 Outdoors & Recreation 0.83
1 Travel & Transport 0.17
2 Arts & Entertainment 0.00
3 College & University 0.00
4 Food 0.00
----M2H: Hillcrest Village----
venue freq
0 Food 0.52
1 Shop & Service 0.34
2 Outdoors & Recreation 0.11
3 Residence 0.02
4 Arts & Entertainment 0.00
----M2J: Fairview, Henry Farm, Oriole----
venue freq
0 Shop & Service 0.59
1 Food 0.33
2 Arts & Entertainment 0.04
3 Nightlife Spot 0.02
4 Professional & Other Places 0.02
----M2K: Bayview Village----
venue freq
0 Food 0.44
1 Shop & Service 0.38
2 Outdoors & Recreation 0.13
3 Travel & Transport 0.04
4 Arts & Entertainment 0.00
----M2L: York Mills, Silver Hills----
venue freq
0 Outdoors & Recreation 0.4
1 Arts & Entertainment 0.2
2 Food 0.2
3 Shop & Service 0.2
4 College & University 0.0
----M2M: Willowdale, Newtonbrook----
venue freq
0 Food 0.72
1 Shop & Service 0.24
2 Nightlife Spot 0.02
3 Outdoors & Recreation 0.02
4 Arts & Entertainment 0.00
----M2N: Willowdale, Willowdale East----
venue freq
0 Food 0.64
1 Shop & Service 0.20
2 Nightlife Spot 0.07
3 Outdoors & Recreation 0.07
4 Arts & Entertainment 0.02
----M2P: York Mills West----
venue freq
0 Food 0.53
1 Outdoors & Recreation 0.18
2 Shop & Service 0.18
3 Nightlife Spot 0.06
4 Travel & Transport 0.06
----M2R: Willowdale, Willowdale West----
venue freq
0 Food 0.45
1 Shop & Service 0.30
2 Outdoors & Recreation 0.20
3 Travel & Transport 0.05
4 Arts & Entertainment 0.00
----M3A: Parkwoods----
venue freq
0 Food 0.37
1 Shop & Service 0.31
2 Travel & Transport 0.17
3 Outdoors & Recreation 0.11
4 Professional & Other Places 0.03
----M3B: Don Mills----
venue freq
0 Food 0.65
1 Shop & Service 0.22
2 Outdoors & Recreation 0.07
3 Nightlife Spot 0.04
4 Travel & Transport 0.02
----M3C: Don Mills----
venue freq
0 Food 0.48
1 Shop & Service 0.22
2 Outdoors & Recreation 0.18
3 Arts & Entertainment 0.10
4 Travel & Transport 0.02
----M3H: Bathurst Manor, Wilson Heights, Downsview North----
venue freq
0 Food 0.47
1 Shop & Service 0.28
2 Outdoors & Recreation 0.17
3 Professional & Other Places 0.06
4 Travel & Transport 0.03
----M3J: Northwood Park, York University----
venue freq
0 Food 0.64
1 Shop & Service 0.23
2 Outdoors & Recreation 0.08
3 Nightlife Spot 0.03
4 Travel & Transport 0.03
----M3K: Downsview----
venue freq
0 Food 0.56
1 Shop & Service 0.19
2 Outdoors & Recreation 0.12
3 Travel & Transport 0.12
4 Arts & Entertainment 0.00
----M3L: Downsview----
venue freq
0 Food 0.43
1 Shop & Service 0.29
2 Outdoors & Recreation 0.14
3 Travel & Transport 0.14
4 Arts & Entertainment 0.00
----M3M: Downsview----
venue freq
0 Food 0.33
1 Outdoors & Recreation 0.33
2 Shop & Service 0.33
3 Arts & Entertainment 0.00
4 College & University 0.00
----M3N: Downsview----
venue freq
0 Food 0.43
1 Shop & Service 0.23
2 Travel & Transport 0.17
3 Outdoors & Recreation 0.10
4 Arts & Entertainment 0.07
----M4A: Victoria Village----
venue freq
0 Food 0.45
1 Shop & Service 0.29
2 Outdoors & Recreation 0.16
3 Travel & Transport 0.06
4 Arts & Entertainment 0.03
----M4B: Parkview Hill, Woodbine Gardens----
venue freq
0 Food 0.45
1 Outdoors & Recreation 0.15
2 Shop & Service 0.15
3 Nightlife Spot 0.10
4 Arts & Entertainment 0.05
----M4C: Woodbine Heights----
venue freq
0 Food 0.43
1 Shop & Service 0.26
2 Outdoors & Recreation 0.20
3 Travel & Transport 0.08
4 Nightlife Spot 0.03
----M4E: The Beaches----
venue freq
0 Food 0.52
1 Shop & Service 0.17
2 Outdoors & Recreation 0.16
3 Nightlife Spot 0.11
4 Arts & Entertainment 0.02
----M4G: Leaside----
venue freq
0 Food 0.50
1 Shop & Service 0.36
2 Nightlife Spot 0.06
3 Outdoors & Recreation 0.06
4 Arts & Entertainment 0.01
----M4H: Thorncliffe Park----
venue freq
0 Food 0.49
1 Shop & Service 0.23
2 Outdoors & Recreation 0.18
3 Nightlife Spot 0.05
4 Arts & Entertainment 0.03
----M4J: East Toronto, Broadview North (Old East York)----
venue freq
0 Food 0.62
1 Shop & Service 0.16
2 Nightlife Spot 0.09
3 Outdoors & Recreation 0.07
4 Arts & Entertainment 0.05
----M4K: The Danforth West, Riverdale----
venue freq
0 Food 0.57
1 Shop & Service 0.19
2 Outdoors & Recreation 0.09
3 Nightlife Spot 0.08
4 Arts & Entertainment 0.05
----M4L: India Bazaar, The Beaches West----
venue freq
0 Food 0.51
1 Shop & Service 0.20
2 Outdoors & Recreation 0.16
3 Nightlife Spot 0.08
4 Travel & Transport 0.04
----M4M: Studio District----
venue freq
0 Food 0.53
1 Shop & Service 0.28
2 Nightlife Spot 0.11
3 Outdoors & Recreation 0.07
4 Travel & Transport 0.01
----M4N: Lawrence Park----
venue freq
0 Food 0.33
1 Outdoors & Recreation 0.33
2 College & University 0.11
3 Shop & Service 0.11
4 Travel & Transport 0.11
----M4P: Davisville North----
venue freq
0 Food 0.56
1 Outdoors & Recreation 0.22
2 Shop & Service 0.11
3 Nightlife Spot 0.06
4 Travel & Transport 0.06
----M4R: North Toronto West, Lawrence Park----
venue freq
0 Food 0.56
1 Shop & Service 0.28
2 Outdoors & Recreation 0.15
3 Arts & Entertainment 0.00
4 College & University 0.00
----M4S: Davisville----
venue freq
0 Food 0.70
1 Shop & Service 0.15
2 Outdoors & Recreation 0.11
3 Nightlife Spot 0.02
4 Travel & Transport 0.02
----M4T: Moore Park, Summerhill East----
venue freq
0 Food 0.50
1 Outdoors & Recreation 0.38
2 Shop & Service 0.12
3 Arts & Entertainment 0.00
4 College & University 0.00
----M4V: Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park----
venue freq
0 Food 0.68
1 Outdoors & Recreation 0.13
2 Shop & Service 0.13
3 Nightlife Spot 0.02
4 Professional & Other Places 0.02
----M4W: Rosedale----
venue freq
0 Outdoors & Recreation 0.67
1 Shop & Service 0.33
2 Arts & Entertainment 0.00
3 College & University 0.00
4 Food 0.00
----M4X: St. James Town, Cabbagetown----
venue freq
0 Food 0.56
1 Shop & Service 0.21
2 Outdoors & Recreation 0.11
3 Nightlife Spot 0.05
4 Arts & Entertainment 0.03
----M4Y: Church and Wellesley----
venue freq
0 Food 0.62
1 Shop & Service 0.14
2 Nightlife Spot 0.10
3 Outdoors & Recreation 0.07
4 Arts & Entertainment 0.05
----M5A: Regent Park, Harbourfront----
venue freq
0 Food 0.53
1 Shop & Service 0.17
2 Outdoors & Recreation 0.10
3 Arts & Entertainment 0.08
4 Nightlife Spot 0.06
----M5B: Garden District, Ryerson----
venue freq
0 Food 0.52
1 Shop & Service 0.27
2 Arts & Entertainment 0.06
3 Outdoors & Recreation 0.06
4 Nightlife Spot 0.04
----M5C: St. James Town----
venue freq
0 Food 0.57
1 Shop & Service 0.22
2 Nightlife Spot 0.07
3 Outdoors & Recreation 0.05
4 Arts & Entertainment 0.04
----M5E: Berczy Park----
venue freq
0 Food 0.52
1 Shop & Service 0.21
2 Nightlife Spot 0.10
3 Arts & Entertainment 0.08
4 Outdoors & Recreation 0.06
----M5G: Central Bay Street----
venue freq
0 Food 0.45
1 Shop & Service 0.31
2 Outdoors & Recreation 0.09
3 Arts & Entertainment 0.06
4 Travel & Transport 0.04
----M5H: Richmond, Adelaide, King----
venue freq
0 Food 0.62
1 Shop & Service 0.13
2 Outdoors & Recreation 0.08
3 Nightlife Spot 0.06
4 Arts & Entertainment 0.05
----M5J: Harbourfront East, Union Station, Toronto Islands----
venue freq
0 Food 0.52
1 Outdoors & Recreation 0.12
2 Travel & Transport 0.12
3 Arts & Entertainment 0.08
4 Nightlife Spot 0.08
----M5M: Bedford Park, Lawrence Manor East----
venue freq
0 Food 0.64
1 Shop & Service 0.27
2 Outdoors & Recreation 0.05
3 Nightlife Spot 0.03
4 Travel & Transport 0.02
----M5N: Roselawn----
venue freq
0 Outdoors & Recreation 1.0
1 Arts & Entertainment 0.0
2 College & University 0.0
3 Food 0.0
4 Nightlife Spot 0.0
----M5P: Forest Hill North & West, Forest Hill Road Park----
venue freq
0 Food 0.54
1 Shop & Service 0.25
2 Outdoors & Recreation 0.21
3 Arts & Entertainment 0.00
4 College & University 0.00
----M5R: The Annex, North Midtown, Yorkville----
venue freq
0 Food 0.70
1 Arts & Entertainment 0.12
2 Shop & Service 0.09
3 Outdoors & Recreation 0.07
4 Nightlife Spot 0.02
----M5S: University of Toronto, Harbord----
venue freq
0 Food 0.63
1 Shop & Service 0.17
2 Nightlife Spot 0.07
3 Outdoors & Recreation 0.06
4 College & University 0.04
----M5T: Kensington Market, Chinatown, Grange Park----
venue freq
0 Food 0.66
1 Shop & Service 0.15
2 Nightlife Spot 0.13
3 Arts & Entertainment 0.03
4 Outdoors & Recreation 0.03
----M5V: CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport----
venue freq
0 Food 0.54
1 Nightlife Spot 0.16
2 Shop & Service 0.12
3 Outdoors & Recreation 0.09
4 Travel & Transport 0.05
----M6A: Lawrence Manor, Lawrence Heights----
venue freq
0 Shop & Service 0.67
1 Food 0.27
2 Travel & Transport 0.04
3 Arts & Entertainment 0.02
4 College & University 0.00
----M6B: Glencairn----
venue freq
0 Food 0.46
1 Shop & Service 0.36
2 Outdoors & Recreation 0.18
3 Arts & Entertainment 0.00
4 College & University 0.00
----M6C: Humewood-Cedarvale----
venue freq
0 Shop & Service 0.38
1 Arts & Entertainment 0.25
2 Outdoors & Recreation 0.25
3 Food 0.12
4 College & University 0.00
----M6E: Caledonia-Fairbanks----
venue freq
0 Food 0.40
1 Shop & Service 0.36
2 Outdoors & Recreation 0.12
3 Travel & Transport 0.12
4 Arts & Entertainment 0.00
----M6G: Christie----
venue freq
0 Food 0.57
1 Shop & Service 0.21
2 Nightlife Spot 0.11
3 Outdoors & Recreation 0.09
4 Professional & Other Places 0.01
----M6H: Dufferin, Dovercourt Village----
venue freq
0 Food 0.53
1 Shop & Service 0.23
2 Nightlife Spot 0.17
3 Outdoors & Recreation 0.05
4 Arts & Entertainment 0.02
----M6J: Little Portugal, Trinity----
venue freq
0 Food 0.62
1 Nightlife Spot 0.16
2 Shop & Service 0.16
3 Arts & Entertainment 0.03
4 Outdoors & Recreation 0.03
----M6K: Brockton, Parkdale Village, Exhibition Place----
venue freq
0 Food 0.51
1 Shop & Service 0.19
2 Nightlife Spot 0.12
3 Outdoors & Recreation 0.07
4 Arts & Entertainment 0.06
----M6L: North Park, Maple Leaf Park, Upwood Park----
venue freq
0 Food 0.59
1 Shop & Service 0.27
2 Outdoors & Recreation 0.09
3 Travel & Transport 0.05
4 Arts & Entertainment 0.00
----M6M: Del Ray, Mount Dennis, Keelsdale and Silverthorn----
venue freq
0 Shop & Service 0.50
1 Food 0.44
2 Outdoors & Recreation 0.06
3 Arts & Entertainment 0.00
4 College & University 0.00
----M6N: Runnymede, The Junction North----
venue freq
0 Food 0.44
1 Shop & Service 0.39
2 Nightlife Spot 0.08
3 Outdoors & Recreation 0.07
4 Travel & Transport 0.01
----M6P: High Park, The Junction South----
venue freq
0 Food 0.56
1 Shop & Service 0.19
2 Nightlife Spot 0.10
3 Outdoors & Recreation 0.08
4 Travel & Transport 0.04
----M6R: Parkdale, Roncesvalles----
venue freq
0 Food 0.56
1 Shop & Service 0.28
2 Nightlife Spot 0.05
3 Outdoors & Recreation 0.05
4 Arts & Entertainment 0.04
----M6S: Runnymede, Swansea----
venue freq
0 Food 0.57
1 Shop & Service 0.21
2 Outdoors & Recreation 0.14
3 Nightlife Spot 0.05
4 Arts & Entertainment 0.03
----M8V: New Toronto, Mimico South, Humber Bay Shores----
venue freq
0 Food 0.56
1 Shop & Service 0.22
2 Outdoors & Recreation 0.14
3 Nightlife Spot 0.06
4 Arts & Entertainment 0.03
----M8W: Alderwood, Long Branch----
venue freq
0 Food 0.50
1 Shop & Service 0.26
2 Outdoors & Recreation 0.12
3 Nightlife Spot 0.06
4 Travel & Transport 0.06
----M8X: The Kingsway, Montgomery Road, Old Mill North----
venue freq
0 Food 0.55
1 Shop & Service 0.26
2 Nightlife Spot 0.06
3 Outdoors & Recreation 0.06
4 Arts & Entertainment 0.04
----M8Y: Old Mill South, King's Mill Park, Sunnylea, Humber Bay, Mimico NE, The Queensway East, Royal York South East, Kingsway Park South East----
venue freq
0 Food 0.47
1 Shop & Service 0.34
2 Outdoors & Recreation 0.13
3 Nightlife Spot 0.03
4 Travel & Transport 0.03
----M8Z: Mimico NW, The Queensway West, South of Bloor, Kingsway Park South West, Royal York South West----
venue freq
0 Food 0.57
1 Shop & Service 0.31
2 Outdoors & Recreation 0.07
3 Arts & Entertainment 0.02
4 Professional & Other Places 0.02
----M9A: Islington Avenue, Humber Valley Village----
venue freq
0 Shop & Service 0.73
1 Food 0.13
2 Outdoors & Recreation 0.13
3 Arts & Entertainment 0.00
4 College & University 0.00
----M9B: West Deane Park, Princess Gardens, Martin Grove, Islington, Cloverdale----
venue freq
0 Food 0.38
1 Shop & Service 0.31
2 Outdoors & Recreation 0.19
3 Travel & Transport 0.12
4 Arts & Entertainment 0.00
----M9C: Eringate, Bloordale Gardens, Old Burnhamthorpe, Markland Wood----
venue freq
0 Shop & Service 0.36
1 Food 0.27
2 Outdoors & Recreation 0.23
3 Arts & Entertainment 0.05
4 College & University 0.05
----M9L: Humber Summit----
venue freq
0 Food 0.58
1 Shop & Service 0.37
2 Outdoors & Recreation 0.05
3 Arts & Entertainment 0.00
4 College & University 0.00
----M9M: Humberlea, Emery----
venue freq
0 Food 0.42
1 Outdoors & Recreation 0.25
2 Shop & Service 0.25
3 Nightlife Spot 0.08
4 Arts & Entertainment 0.00
----M9N: Weston----
venue freq
0 Food 0.56
1 Shop & Service 0.22
2 Outdoors & Recreation 0.11
3 Travel & Transport 0.11
4 Arts & Entertainment 0.00
----M9P: Westmount----
venue freq
0 Food 0.41
1 Shop & Service 0.29
2 Outdoors & Recreation 0.24
3 Travel & Transport 0.06
4 Arts & Entertainment 0.00
----M9R: Kingsview Village, St. Phillips, Martin Grove Gardens, Richview Gardens----
venue freq
0 Shop & Service 0.54
1 Food 0.35
2 Outdoors & Recreation 0.08
3 Arts & Entertainment 0.04
4 College & University 0.00
----M9V: South Steeles, Silverstone, Humbergate, Jamestown, Mount Olive, Beaumond Heights, Thistletown, Albion Gardens----
venue freq
0 Food 0.63
1 Shop & Service 0.26
2 Travel & Transport 0.11
3 Arts & Entertainment 0.00
4 College & University 0.00
----M9W: Northwest, West Humber - Clairville----
venue freq
0 Food 0.65
1 Shop & Service 0.29
2 Outdoors & Recreation 0.06
3 Arts & Entertainment 0.00
4 College & University 0.00
def return_most_common_venues(row, num_top_venues):
row_categories = row.iloc[1:]
row_categories_sorted = row_categories.sort_values(ascending=False)
return row_categories_sorted.index.values[0:num_top_venues]
num_top_venues = 10
indicators = ['st', 'nd', 'rd']
# create columns according to number of top venues
columns = ['Postal Code']
for ind in np.arange(num_top_venues):
try:
columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
except:
columns.append('{}th Most Common Venue'.format(ind+1))
# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Postal Code'] = toronto_grouped['Postal Code']
for ind in np.arange(toronto_grouped.shape[0]):
neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)
neighborhoods_venues_sorted.head()
| Postal Code | 1st Most Common Venue | 2nd Most Common Venue | 3rd Most Common Venue | 4th Most Common Venue | 5th Most Common Venue | 6th Most Common Venue | 7th Most Common Venue | 8th Most Common Venue | 9th Most Common Venue | 10th Most Common Venue | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | M1B | Zoo Exhibit | Fast Food Restaurant | Trail | Hobby Shop | Restaurant | Coffee Shop | Park | Paper / Office Supplies Store | Caribbean Restaurant | Spa |
| 1 | M1C | Park | Italian Restaurant | Playground | Neighborhood | Breakfast Spot | Gym | Gym / Fitness Center | Pharmacy | Pizza Place | Bank |
| 2 | M1E | Pizza Place | Park | Restaurant | Breakfast Spot | Electronics Store | Fast Food Restaurant | Hotel | Bank | Chinese Restaurant | Thrift / Vintage Store |
| 3 | M1G | Park | Coffee Shop | Pharmacy | Juice Bar | Fast Food Restaurant | Mobile Phone Shop | Chinese Restaurant | ATM | Other Repair Shop | Paper / Office Supplies Store |
| 4 | M1H | Indian Restaurant | Coffee Shop | Burger Joint | Playground | Bus Line | Music Store | German Restaurant | Fried Chicken Joint | Caribbean Restaurant | Chinese Restaurant |
Returning to feature generation, it might be useful to include the overall areal density of venues as a feature. This can be done using the radius that was required to get the target number of venues returned when we listed the venues in each postal code. This method is imperfect because of the results massaging done by Foursquare, but is the best we can do with that data source.
df_venue_density = df_venues[['Postal Code','Radius']].groupby('Postal Code').mean(numeric_only=False).reset_index().rename(columns={'Radius':'Venue Radius'})
df_venue_density = df_venue_density.merge(df_venues[['Postal Code','Radius']].groupby('Postal Code').count().reset_index().rename(columns={'Radius':'Venue Count'}),on='Postal Code')
df_venue_density['Venue Density'] = df_venue_density['Venue Count']/(np.pi*(df_venue_density['Venue Radius']/1000)**2)
df_venue_density['Log10 (Venue Density)'] = df_venue_density['Venue Density'].apply(np.log10)
print(f"Venue density varies from {min(df_venue_density['Venue Density']):.3f} to {max(df_venue_density['Venue Density']):.3f} with mean {np.mean(df_venue_density['Venue Density']):.3f} and standard deviation {np.std(df_venue_density['Venue Density']):.3f}")
display(df_venue_density.head())
fig, axs = plt.subplots(1,2,figsize=(12,5))
ax = axs[0]
ax.hist(x=df_venue_density['Venue Density'],bins=20,log=False)
ax.set_xlabel('Venue Density [#/km$^2$]')
ax.set_ylabel('Number of Postal Codes')
ax = axs[1]
ax.hist(x=df_venue_density['Log10 (Venue Density)'],bins=20,log=False)
ax.set_xlabel('Log10 (Venue Density [#/km$^2$])')
ax.set_ylabel('Number of Postal Codes');
Venue density varies from 0.849 to 127.324 with mean 24.428 and standard deviation 31.488
| Postal Code | Venue Radius | Venue Count | Venue Density | Log10 (Venue Density) | |
|---|---|---|---|---|---|
| 0 | M1B | 1500.0 | 31 | 4.385603 | 0.642029 |
| 1 | M1C | 1500.0 | 13 | 1.839124 | 0.264611 |
| 2 | M1E | 1500.0 | 49 | 6.932082 | 0.840864 |
| 3 | M1G | 1000.0 | 9 | 2.864789 | 0.457093 |
| 4 | M1H | 1000.0 | 23 | 7.321127 | 0.864578 |
The distribution of the logarithm of venue density looks more normal and therefore is expected to be better suited as a feature for clustering, so we will use that below.
Here we construct a list of inputs to our clustering algorithm, with the most relevant entry being various feature sets it will be interesting to fit to, including:
Note that giving different scale factors to various features might result in improved fits - this might be worth exploring in a future work.
k_means_data = []
POSTAL_CODES = toronto_grouped
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import QuantileTransformer
scaler = MinMaxScaler() # StandardScaler(), RobustScaler(), MinMaxScaler(), QuantileTransformer(output_distribution='normal', random_state=0)
# Order:
# 0- 4: Venues, no scaling
# 5- 9: Venues, Census, VDensity, scaled
# 10-14: Venues, Census, VDensity, scaled with additional Venues factor
# 15: Census data, scaled
# 16: Venue density, scaled
# 17: Census data and Venue density, scaled
# Add entries for Venues at various category depths
for i in range(max(df_venues['Category Depth'])+1):
features = locals()[f"toronto_grouped_{i}"]
add_dict = {'name':f"Venues Depth {i}",
'labels':features['Postal Code'],
'features':features.drop(columns='Postal Code')
}
k_means_data.append(add_dict)
'''
# Duplicate the above and add the census features from df_final
for i in range(max(df_venues['Category Depth'])+1):
features = locals()[f"toronto_grouped_{i}"].merge(df_final[['Postal Code','Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']],on='Postal Code')
add_dict = {'name':f"Venues Depth {i} with Census Data",
'labels':features['Postal Code'],
'features':features.drop('Postal Code',1)
}
k_means_data.append(add_dict)
# Duplicate the above and add the Foursquare venue density
for i in range(max(df_venues['Category Depth'])+1):
features = locals()[f"toronto_grouped_{i}"].merge(df_final[['Postal Code','Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']],on='Postal Code').merge(df_venue_density[['Postal Code','Log10 (Venue Density)']],on='Postal Code')
add_dict = {'name':f"Venues Depth {i} with Census Data and Venue Density",
'labels':features['Postal Code'],
'features':features.drop('Postal Code',1)
}
k_means_data.append(add_dict)
# Duplicate the above but also normalize the non-venue-vector features
scale_column_names = ['Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)','Log10 (Venue Density)']
for i in range(max(df_venues['Category Depth'])+1):
features = locals()[f"toronto_grouped_{i}"].merge(df_final[['Postal Code','Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']],on='Postal Code').merge(df_venue_density[['Postal Code','Log10 (Venue Density)']],on='Postal Code')
add_dict = {'name':f"Venues Depth {i} with scaled Census Data and Venue Density",
'labels':features['Postal Code'],
'features':features.drop('Postal Code',1)
}
add_dict['features'][scale_column_names] = scaler.fit_transform(add_dict['features'][scale_column_names])
k_means_data.append(add_dict)
'''
# Venues scaled globally, Census data and Venue density scaled
scale_column_names = ['Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)','Log10 (Venue Density)']
for i in range(max(df_venues['Category Depth'])+1):
features = locals()[f"toronto_grouped_{i}"]
vectors = features.drop(columns='Postal Code').values
vectors = scaler.fit_transform(vectors.reshape(-1,1)).reshape(*list(vectors.shape))
vectors = vectors
features[features.columns.drop('Postal Code')] = vectors
features = features.merge(df_final[['Postal Code','Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']],on='Postal Code').merge(df_venue_density[['Postal Code','Log10 (Venue Density)']],on='Postal Code')
add_dict = {'name':f"Scaled Venues Depth {i}\n with scaled Census Data\n and Venue Density",
'labels':features['Postal Code'],
'features':features.drop(columns='Postal Code')
}
add_dict['features'][scale_column_names] = scaler.fit_transform(add_dict['features'][scale_column_names])
k_means_data.append(add_dict)
# Venues scaled globally with additional factor, Census data and Venue density scaled;
from sklearn.preprocessing import StandardScaler
scale_column_names = ['Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)','Log10 (Venue Density)']
for i in range(max(df_venues['Category Depth'])+1):
features = locals()[f"toronto_grouped_{i}"]
vectors = features.drop(columns='Postal Code').values
vectors = scaler.fit_transform(vectors.reshape(-1,1)).reshape(*list(vectors.shape))
vectors = vectors*1.4 # This factor increases MSS by about the same amount as the census and venue density does
features[features.columns.drop('Postal Code')] = vectors
features = features.merge(df_final[['Postal Code','Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']],on='Postal Code').merge(df_venue_density[['Postal Code','Log10 (Venue Density)']],on='Postal Code')
add_dict = {'name':f"Super-Scaled Venues Depth {i}\n with scaled Census Data\n and Venue Density",
'labels':features['Postal Code'],
'features':features.drop(columns='Postal Code')
}
add_dict['features'][scale_column_names] = scaler.fit_transform(add_dict['features'][scale_column_names])
k_means_data.append(add_dict)
# Census data only, scaled
scale_column_names = ['Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']
features = df_final[['Postal Code','Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']]
add_dict = {'name':f"Scaled Census Data",
'labels':features['Postal Code'],
'features':features.drop(columns='Postal Code')
}
add_dict['features'][scale_column_names] = scaler.fit_transform(add_dict['features'][scale_column_names])
k_means_data.append(add_dict)
# Venue density only, scaled
scale_column_names = ['Log10 (Venue Density)']
features = df_venue_density[['Postal Code','Log10 (Venue Density)']]
add_dict = {'name':f"Scaled Venue Density",
'labels':features['Postal Code'],
'features':features.drop(columns='Postal Code')
}
add_dict['features'][scale_column_names] = scaler.fit_transform(add_dict['features'][scale_column_names])
k_means_data.append(add_dict)
# Census and Venue Density, scaled
scale_column_names = ['Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)','Log10 (Venue Density)']
features = df_final[['Postal Code','Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)']].merge(df_venue_density[['Postal Code','Log10 (Venue Density)']],on='Postal Code')
add_dict = {'name':f"Scaled Census Data and Venue Density",
'labels':features['Postal Code'],
'features':features.drop(columns='Postal Code')
}
add_dict['features'][scale_column_names] = scaler.fit_transform(add_dict['features'][scale_column_names])
k_means_data.append(add_dict)
We will perform a cluster number optimization on all the feature sets previously specified to determine an optimal cluster number k and optimal feature set.
We will first use the elbow method to get an idea of the optimal k, looking at the mean distance to a centroid vs k to see the elbow in a continuously decreasing line. After that we will look at the gap statistic to get a more quantitative measure of the optimal k.
%%time
from sklearn.cluster import KMeans
kmeans_params = {'ks':list(range(1,41)),
'random_state':0,
'n_init':12
}
for i, param_dict in enumerate(k_means_data):
print(f"Beginning fit for feature set {i+1} / {len(k_means_data)}")
name = param_dict['name']
features = param_dict['features']
param_dict['model_list'] = []
param_dict['inertia'] = []
for k in kmeans_params['ks']:
kmeans_model = KMeans(n_clusters=k, random_state=kmeans_params['random_state'], n_init=kmeans_params['n_init'])
kmeans_model.fit(features)
# print(k,kmeans_model.n_iter_)
param_dict['model_list'].append(kmeans_model)
param_dict['inertia'].append(kmeans_model.inertia_)
print('Clustering complete')
Beginning fit for feature set 1 / 18 Beginning fit for feature set 2 / 18 Beginning fit for feature set 3 / 18 Beginning fit for feature set 4 / 18 Beginning fit for feature set 5 / 18 Beginning fit for feature set 6 / 18 Beginning fit for feature set 7 / 18 Beginning fit for feature set 8 / 18 Beginning fit for feature set 9 / 18 Beginning fit for feature set 10 / 18 Beginning fit for feature set 11 / 18 Beginning fit for feature set 12 / 18 Beginning fit for feature set 13 / 18 Beginning fit for feature set 14 / 18 Beginning fit for feature set 15 / 18 Beginning fit for feature set 16 / 18 Beginning fit for feature set 17 / 18 Beginning fit for feature set 18 / 18 Clustering complete Wall time: 1min 52s
We now plot the sum of intra-cluster sum of square distances to centroids. The elbow in these plots can be taken as the optimal k. We notice that:
Unfortunately, the elbow method falls victim to the issue of scaling - elbow location according to maximum curvature or matching slope of the data range (or the average angle between linear approximations of the beginning and ending of the data) have the caveat that an overall scaling factor changes the location of the match condition.
fig, axs = plt.subplots(len(k_means_data)//5+1,5,figsize=(20,25))
for i, param_dict in enumerate(k_means_data):
name = param_dict['name']
ax = axs[i//5][i%5]
ax.plot(kmeans_params['ks'],param_dict['inertia'])
ax.set_xlabel("Number of Clusters")
ax.set_ylabel("MSS Distance to Centroids")
ax.set_title(f"{name}")
ax.set_ylim(bottom=0)
plt.tight_layout()
This section uses the gap statistic, which measures the difference between the sum of squared distances to assigned centroids for the data and for a similar featureset with uniformly distributed values (that is, without any cluster information). The optimal k is chosen as the lowest value of k for which the statistic is higher than that of the next value of k less the standard deviation of the statistic at the next value of k. In other words, k is chosen at the point of diminishing informational return from adding the next cluster. See here for further description.
Note that in this section text and graphics may not correspond due to nonreproducibility of the numpy random number generator; the text corresponds to one instance of execution.
First we define some functions to calculate and display the gap statistic.
# Gap Statistic for K means, from online (https://anaconda.org/milesgranger/gap-statistic/notebook)
def optimalK(data, nrefs=3, maxClusters=40):
"""
Calculates KMeans optimal K using Gap Statistic
Params:
data: ndarry of shape (n_samples, n_features)
nrefs: number of sample reference datasets to create
maxClusters: Maximum number of clusters to test for
Returns: (gaps, optimalK)
"""
gaps = np.zeros((len(range(1, maxClusters)),))
resultsdf = pd.DataFrame({'clusterCount':[], 'gap':[], 'gap_std':[]})
for gap_index, k in enumerate(range(1, maxClusters)):# Holder for reference dispersion results
refDisps = np.zeros(nrefs)# For n references, generate random sample and perform kmeans getting resulting dispersion of each loop
for i in range(nrefs):
# Create new random reference set
randomReference = np.random.random_sample(size=data.shape)
# Fit to it
km = KMeans(k)
km.fit(randomReference)
refDisp = km.inertia_
refDisps[i] = refDisp# Fit cluster to original data and create dispersion
km = KMeans(k)
km.fit(data)
origDisp = km.inertia_# Calculate gap statistic
gap = np.mean(np.log(refDisps)) - np.log(origDisp)# Assign this loop's gap statistic to gaps
gaps[gap_index] = gap
gap_all = []
for refDisp in refDisps:
gap_all.append(np.log(refDisp) - np.log(origDisp))
resultsdf = resultsdf.append({'clusterCount':k, 'gap':gap, 'gap_std':np.std(gap_all)}, ignore_index=True)
return (gaps.argmax() + 1, resultsdf)
def plotGapStatistic(data, nrefs=3, maxClusters=40, name=''):
score_g, dfp = optimalK(data, nrefs, maxClusters)
fix, axs = plt.subplots(1,2, figsize=(10,4))
plt.sca(axs[0])
plt.errorbar(dfp['clusterCount'], dfp['gap'], dfp['gap_std'], linestyle='--', marker='o', color='b');
plt.xticks(dfp['clusterCount'])
plt.xlabel('K');
plt.ylabel('Gap Statistic');
plt.title(f"Dataset: {name},\nReferences: {nrefs}");
plt.sca(axs[1])
x = dfp['clusterCount'][0:-2]
y = dfp['gap'].values[0:-2]-(dfp['gap'].values[1:-1]-dfp['gap_std'].values[1:-1])
plt.errorbar(x, y, dfp['gap_std'].values[0:-2], linestyle='-', marker='o', color='b', label="Adjusted Gap Difference")
plt.plot([min(x),max(x)],[0]*2,color='r', label='Threshold')
try:
optk = np.where(y>0)[0][0]+1
plt.plot([optk]*2,[min(y),max(y)],linestyle='--',color='k',label="k$_{opt}$ = "+f"{optk}")
except:
pass
plt.xticks(x)
plt.xlabel('K');
plt.ylabel('G[k]-(G[k+1]-std(G[k+1]))')
plt.title('Gap Statistic Selection')
plt.legend()
plt.tight_layout()
Let's vary the number of reference distributions to see how the optimal k prediction becomes less noisy.
Notice how the standard deviation of the reference distribution increases with the number of reference distributions, but the variation of the mean decreases.
We see that the optimal k is almost 6, but more reliably 8 or 9. There is variation due to the state of the random number generator (though this was fixed for the k means fitting).
plotGapStatistic(k_means_data[0]['features'], nrefs=5, maxClusters=20, name=k_means_data[0]['name'])
plotGapStatistic(k_means_data[0]['features'], nrefs=20, maxClusters=20, name=k_means_data[0]['name'])
plotGapStatistic(k_means_data[0]['features'], nrefs=80, maxClusters=20, name=k_means_data[0]['name'])
plotGapStatistic(k_means_data[0]['features'], nrefs=240, maxClusters=20, name=k_means_data[0]['name'])
Next let's look at how the venue depth changes the gap statistic. This is a bit unusual because the dimensionality increases greatly as the features change from a dense to a sparse vector. The appropriateness of the gap statistic for sparse features has not been determined, and is assumed suspect.
Notice that with increasing depth the optimal k increases: 6, 10, 13, 14, 14. *Note values may display differently due to altered random states.
plotGapStatistic(k_means_data[0]['features'], nrefs=5, maxClusters=20, name=k_means_data[0]['name'])
plotGapStatistic(k_means_data[1]['features'], nrefs=5, maxClusters=20, name=k_means_data[1]['name'])
plotGapStatistic(k_means_data[2]['features'], nrefs=5, maxClusters=20, name=k_means_data[2]['name'])
plotGapStatistic(k_means_data[3]['features'], nrefs=5, maxClusters=20, name=k_means_data[3]['name'])
plotGapStatistic(k_means_data[4]['features'], nrefs=5, maxClusters=20, name=k_means_data[4]['name'])
As the depth increases, this corresponds to an increasingly skew knee location choice.
gap_opt_k = [6,10,13,14,14]
fig, axs = plt.subplots(1,5,figsize=(20,6))
for i, param_dict in enumerate(k_means_data[10:15]):
name = param_dict['name']
ax = axs[i%5]
ax.plot(kmeans_params['ks'],param_dict['inertia'])
ax.plot([gap_opt_k[i]]*2,[0,max(param_dict['inertia'])],linestyle='--',color='k',label="k$_{opt}$ = "+f"{gap_opt_k[i]}")
ax.set_xlabel("Number of Clusters")
ax.set_ylabel("MSS Distance to Centroids")
ax.set_title(f"{name}")
ax.set_ylim(bottom=0,top=35)
ax.set_xlim(right=30)
plt.sca(ax)
plt.legend(loc='upper right')
plt.tight_layout()
Let's examine other feature combinations we fit to above.
The optimum k here is 7 or 8, slightly higher than the venues-only choice, with lower variability than the venues-only curves.
plotGapStatistic(k_means_data[5]['features'], nrefs=80, maxClusters=20, name=k_means_data[5]['name'])
plotGapStatistic(k_means_data[10]['features'], nrefs=80, maxClusters=20, name=k_means_data[10]['name'])
These features were processed to be nearly normal, so it is not surprising that they result in low optimal cluster numbers.
The optimal k is 2 when census data is involved, and 1 when only venue density is considered. This result is expected, since the features were adjusted to be relatively normal in the preprocessing stage.
plotGapStatistic(k_means_data[15]['features'], nrefs=80, maxClusters=20, name=k_means_data[15]['name'])
plotGapStatistic(k_means_data[16]['features'], nrefs=80, maxClusters=20, name=k_means_data[16]['name'])
plotGapStatistic(k_means_data[17]['features'], nrefs=80, maxClusters=20, name=k_means_data[17]['name'])
There are many ways to evaluate the optimal k, and evaluation of the best evaluation metric is beyond the current scope of this project. A good jumping-off point is the yellowbrick module, which implements several elbow detection methods. There are other sources pointing to the gap statistic and silhouette coefficient, as well as a host of other metrics, which can be used to choose optimal k and are often implemented in modules or libraries (see here and here). Adapting the statistics or preprocessing of the data for vector features instead of column features will require some thought, as examples I've found have seemed to assume normalized column features.
From the preceding section using elbow inspection and the gap statistic, we select dataset 10 (venue vector globally scaled to 1.4 range, census and venue density data scaled to 1.0 range), and k~8 as near-optimal model inputs.
Can the clustering procedure be refined any more?
Using the ANOVA F-test we can evaluate which features are important for the clustering decision.
We begin by defining some useful display functions:
plim = 0.05
def highlight_below_plim(val):
"""
Takes a scalar and returns a string with
the css property `'background-color: yellow'` for values below
plim local variable, '' otherwise.
"""
background_color = 'yellow' if val < plim else 'None'
return 'background-color: %s' % background_color
def highlight_zero_red(val):
background_color = 'red' if val <= 0 else 'None'
return 'background-color: %s' % background_color
def showPValues(featureset):
name = k_means_data[featureset]['name']
models = k_means_data[featureset]['model_list']
features = k_means_data[featureset]['features']
labels = k_means_data[featureset]['labels']
newname = name.replace('\n','')
print(f"P-Values thresholded at {plim:0.2} for features: {newname}")
df_eval_fs = pd.DataFrame(index=kmeans_params['ks'],columns=features.columns)
for k in kmeans_params['ks']:
clusters = models[k-1].labels_
F, pval = sklearn.feature_selection.f_classif(features,clusters)
df_eval_fs.loc[k,:] = pval
display(df_eval_fs.style.applymap(highlight_below_plim).applymap(highlight_zero_red).format("{:.2e}"))
import warnings
warnings.filterwarnings('ignore')
For the selected feature set we display the p-values (probability that the feature distributions come from the global distribution under the assumption of normality). P-values below 5% are highlighted yellow, and indicate the feature is significant.
showPValues(10) # Selected feature set
P-Values thresholded at 0.05 for features: Super-Scaled Venues Depth 0 with scaled Census Data and Venue Density
| Arts & Entertainment | College & University | Food | Nightlife Spot | Outdoors & Recreation | Professional & Other Places | Residence | Shop & Service | Travel & Transport | Dwelling Density | Log10 (Population Density) | Log10 (1 - Residentiality) | Log10 (Venue Density) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2 | 5.31e-01 | 7.03e-01 | 1.01e-04 | 1.27e-12 | 1.68e-02 | 2.55e-01 | 7.34e-01 | 3.57e-04 | 1.08e-02 | 7.25e-16 | 3.67e-12 | 2.47e-09 | 1.92e-20 |
| 3 | 6.94e-01 | 7.99e-01 | 5.38e-11 | 3.69e-12 | 3.12e-23 | 3.72e-01 | 9.09e-01 | 2.07e-05 | 8.28e-02 | 4.78e-16 | 8.35e-13 | 5.98e-08 | 5.67e-22 |
| 4 | 1.37e-01 | 6.51e-01 | 1.08e-22 | 1.33e-11 | 9.39e-23 | 4.28e-01 | 7.91e-01 | 5.14e-10 | 6.20e-02 | 1.29e-14 | 2.50e-12 | 4.26e-07 | 1.22e-23 |
| 5 | 2.45e-05 | 8.05e-03 | 3.28e-24 | 7.88e-11 | 5.57e-31 | 4.67e-01 | 9.48e-01 | 1.56e-13 | 6.46e-02 | 5.98e-15 | 2.64e-11 | 8.70e-08 | 5.11e-22 |
| 6 | 7.73e-02 | 5.77e-01 | 2.72e-24 | 1.58e-10 | 7.81e-31 | 1.52e-01 | 7.89e-01 | 2.55e-14 | 2.79e-01 | 5.43e-17 | 4.68e-13 | 1.36e-12 | 2.46e-24 |
| 7 | 1.61e-04 | 4.10e-01 | 8.77e-27 | 2.32e-10 | 7.27e-31 | 2.56e-01 | 9.01e-01 | 5.71e-19 | 4.55e-03 | 3.40e-16 | 3.68e-13 | 3.30e-09 | 7.92e-29 |
| 8 | 5.80e-04 | 9.38e-02 | 2.27e-30 | 9.20e-10 | 1.81e-33 | 2.11e-01 | 9.27e-01 | 1.73e-17 | 3.31e-05 | 1.15e-19 | 3.59e-13 | 1.11e-08 | 3.50e-27 |
| 9 | 1.37e-15 | 6.48e-01 | 1.42e-28 | 3.01e-09 | 2.44e-28 | 2.21e-01 | 9.62e-01 | 3.21e-18 | 2.18e-03 | 4.45e-20 | 1.64e-14 | 8.81e-10 | 2.15e-26 |
| 10 | 2.59e-05 | 7.39e-01 | 5.68e-29 | 5.96e-09 | 1.96e-30 | 2.50e-01 | 9.67e-01 | 8.69e-16 | 3.76e-05 | 5.45e-19 | 2.18e-18 | 1.18e-11 | 2.07e-24 |
| 11 | 1.45e-22 | 3.98e-01 | 1.75e-32 | 1.55e-09 | 9.16e-33 | 3.01e-01 | 9.70e-01 | 9.69e-15 | 7.22e-04 | 6.01e-18 | 5.60e-17 | 1.13e-09 | 3.30e-25 |
| 12 | 1.33e-14 | 3.09e-01 | 9.06e-31 | 1.07e-09 | 2.12e-31 | 4.66e-01 | 9.92e-01 | 7.86e-15 | 4.31e-06 | 2.05e-23 | 2.75e-16 | 5.76e-06 | 3.27e-30 |
| 13 | 1.90e-14 | 9.61e-01 | 1.83e-27 | 3.05e-07 | 5.19e-30 | 2.66e-01 | 9.96e-01 | 1.09e-17 | 1.95e-04 | 6.35e-20 | 3.76e-18 | 7.14e-12 | 1.90e-27 |
| 14 | 2.75e-14 | 5.80e-02 | 4.14e-27 | 8.69e-09 | 4.14e-25 | 2.79e-01 | 9.93e-01 | 3.89e-16 | 3.11e-04 | 1.80e-26 | 3.90e-18 | 9.19e-15 | 4.76e-28 |
| 15 | 8.54e-14 | 7.30e-01 | 3.28e-27 | 1.28e-07 | 5.75e-28 | 3.05e-01 | 9.66e-01 | 2.75e-16 | 1.92e-05 | 1.51e-22 | 4.72e-22 | 1.11e-15 | 2.09e-27 |
| 16 | 1.98e-11 | 5.34e-01 | 2.84e-25 | 6.81e-07 | 4.52e-32 | 2.83e-01 | 9.93e-01 | 1.05e-16 | 6.71e-03 | 3.91e-21 | 1.12e-21 | 9.33e-15 | 1.46e-31 |
| 17 | 2.96e-19 | 5.03e-01 | 1.55e-29 | 4.51e-08 | 4.62e-33 | 5.23e-01 | 9.99e-01 | 1.01e-20 | 1.02e-02 | 7.62e-23 | 5.83e-17 | 9.35e-14 | 5.14e-27 |
| 18 | 2.77e-19 | 8.20e-01 | 4.18e-29 | 2.61e-05 | 1.11e-29 | 3.93e-01 | 9.77e-01 | 4.18e-21 | 7.63e-06 | 8.44e-22 | 8.16e-21 | 2.17e-12 | 1.58e-28 |
| 19 | 3.23e-19 | 8.39e-01 | 2.03e-24 | 1.51e-05 | 7.58e-35 | 4.84e-01 | 9.97e-01 | 1.02e-18 | 2.26e-05 | 2.56e-22 | 6.55e-21 | 2.75e-15 | 7.19e-31 |
| 20 | 4.54e-04 | 6.91e-01 | 7.70e-29 | 1.33e-06 | 5.43e-32 | 2.06e-01 | 9.98e-01 | 1.53e-19 | 7.64e-03 | 1.40e-23 | 3.18e-22 | 1.18e-11 | 4.00e-35 |
| 21 | 1.27e-18 | 7.18e-01 | 4.39e-27 | 1.84e-05 | 3.61e-30 | 6.46e-01 | 1.00e+00 | 2.92e-18 | 3.61e-10 | 3.53e-23 | 3.59e-20 | 1.45e-15 | 2.09e-33 |
| 22 | 9.65e-17 | 3.57e-01 | 8.83e-29 | 2.25e-05 | 4.04e-30 | 6.34e-01 | 9.92e-01 | 1.86e-20 | 1.21e-03 | 7.10e-21 | 3.62e-18 | 7.39e-13 | 1.82e-37 |
| 23 | 9.49e-18 | 7.74e-01 | 1.14e-26 | 2.24e-05 | 1.12e-31 | 5.02e-01 | 9.84e-01 | 8.80e-20 | 1.00e-07 | 3.56e-20 | 1.78e-24 | 1.93e-13 | 8.27e-31 |
| 24 | 9.24e-26 | 2.92e-01 | 2.16e-24 | 2.00e-05 | 4.01e-29 | 8.61e-01 | 1.00e+00 | 6.52e-21 | 1.09e-02 | 2.81e-22 | 1.97e-24 | 1.15e-14 | 5.13e-33 |
| 25 | 4.51e-23 | 9.75e-01 | 1.84e-25 | 9.69e-07 | 5.70e-33 | 8.77e-01 | 1.00e+00 | 3.13e-18 | 2.21e-02 | 9.04e-26 | 1.12e-20 | 1.21e-14 | 4.49e-33 |
| 26 | 4.45e-24 | 1.05e-01 | 2.14e-27 | 5.99e-08 | 2.50e-32 | 6.01e-01 | 7.15e-01 | 1.90e-21 | 6.64e-03 | 8.00e-20 | 9.26e-21 | 2.65e-13 | 1.54e-31 |
| 27 | 5.90e-18 | 2.80e-10 | 1.01e-26 | 1.24e-04 | 3.24e-32 | 9.38e-02 | 1.00e+00 | 2.66e-19 | 1.39e-08 | 4.17e-23 | 1.14e-21 | 2.01e-13 | 8.89e-28 |
| 28 | 1.49e-19 | 5.10e-01 | 7.45e-25 | 3.11e-05 | 1.77e-30 | 3.76e-02 | 1.00e+00 | 6.57e-20 | 2.73e-06 | 1.88e-20 | 1.17e-21 | 6.20e-16 | 5.71e-31 |
| 29 | 2.43e-21 | 3.00e-02 | 1.67e-26 | 2.40e-06 | 8.56e-33 | 4.63e-01 | 1.00e+00 | 6.18e-19 | 2.02e-07 | 1.87e-22 | 1.81e-21 | 1.95e-11 | 2.34e-30 |
| 30 | 1.16e-16 | 3.90e-11 | 5.38e-24 | 3.33e-05 | 5.01e-31 | 1.41e-01 | 9.89e-01 | 1.21e-19 | 5.98e-10 | 1.75e-20 | 1.24e-19 | 2.44e-18 | 4.32e-29 |
| 31 | 4.84e-24 | 1.71e-16 | 4.01e-26 | 5.48e-04 | 6.36e-27 | 1.53e-01 | 1.00e+00 | 1.59e-20 | 1.05e-06 | 1.44e-23 | 3.32e-21 | 6.70e-14 | 8.67e-29 |
| 32 | 8.72e-13 | 5.61e-15 | 9.91e-29 | 8.82e-07 | 2.83e-26 | 5.76e-01 | 9.98e-01 | 2.87e-22 | 9.88e-08 | 4.10e-23 | 5.53e-20 | 3.32e-14 | 6.65e-28 |
| 33 | 1.67e-20 | 3.31e-09 | 2.04e-26 | 2.85e-05 | 1.74e-33 | 2.21e-01 | 1.00e+00 | 2.80e-19 | 4.15e-10 | 5.08e-22 | 6.84e-22 | 6.77e-12 | 8.65e-28 |
| 34 | 2.11e-21 | 3.34e-08 | 7.18e-25 | 1.57e-03 | 9.34e-30 | 1.85e-01 | 1.00e+00 | 7.55e-18 | 1.50e-07 | 4.46e-20 | 1.63e-19 | 4.15e-17 | 2.20e-28 |
| 35 | 8.07e-16 | 4.51e-13 | 2.61e-22 | 3.00e-07 | 4.19e-30 | 7.34e-01 | 1.00e+00 | 3.64e-21 | 8.01e-09 | 1.77e-19 | 8.18e-19 | 3.63e-16 | 4.92e-29 |
| 36 | 1.33e-18 | 1.03e-16 | 1.36e-26 | 4.84e-05 | 2.41e-31 | 2.89e-01 | 9.79e-01 | 1.29e-17 | 1.61e-09 | 1.10e-22 | 1.12e-17 | 1.08e-13 | 4.27e-29 |
| 37 | 5.70e-18 | 3.56e-09 | 4.98e-25 | 6.81e-08 | 6.49e-29 | 1.90e-01 | 1.00e+00 | 8.53e-18 | 3.59e-08 | 7.51e-23 | 2.05e-19 | 2.30e-14 | 9.72e-28 |
| 38 | 1.87e-18 | 5.41e-06 | 1.44e-23 | 1.43e-05 | 3.78e-30 | 8.19e-01 | 1.00e+00 | 1.97e-18 | 4.18e-10 | 1.31e-21 | 5.10e-19 | 9.15e-12 | 5.32e-28 |
| 39 | 3.46e-19 | 8.09e-12 | 4.99e-23 | 1.62e-05 | 4.87e-29 | 2.86e-01 | 1.00e+00 | 8.10e-20 | 3.25e-07 | 1.32e-20 | 3.93e-18 | 5.53e-17 | 1.05e-25 |
| 40 | 5.62e-18 | 1.75e-13 | 7.64e-27 | 2.20e-04 | 4.42e-28 | 3.29e-01 | 1.00e+00 | 1.26e-20 | 1.73e-07 | 1.19e-20 | 3.75e-16 | 1.17e-16 | 1.18e-24 |
Clearly, the less important features are:
Let's see if the same trend shows for the venue vector at various depths:
showPValues(0) # Venues Depth 0
P-Values thresholded at 0.05 for features: Venues Depth 0
| Arts & Entertainment | College & University | Food | Nightlife Spot | Outdoors & Recreation | Professional & Other Places | Residence | Shop & Service | Travel & Transport | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2 | 3.47e-04 | 3.07e-03 | 4.44e-18 | 3.96e-03 | 5.96e-15 | 1.20e-01 | 6.13e-01 | 9.20e-01 | 5.77e-01 |
| 3 | 2.87e-01 | 5.87e-01 | 2.26e-28 | 1.50e-04 | 5.99e-25 | 6.42e-01 | 5.67e-01 | 1.56e-09 | 3.12e-02 |
| 4 | 2.70e-01 | 6.47e-01 | 1.73e-28 | 7.09e-05 | 3.54e-28 | 8.02e-01 | 7.58e-01 | 9.17e-19 | 8.30e-03 |
| 5 | 1.00e-04 | 7.51e-03 | 1.35e-30 | 8.62e-06 | 6.91e-35 | 6.45e-01 | 9.55e-01 | 8.32e-23 | 3.01e-02 |
| 6 | 6.29e-23 | 5.24e-02 | 4.65e-34 | 8.27e-04 | 4.72e-35 | 7.79e-01 | 9.66e-01 | 6.29e-21 | 1.09e-03 |
| 7 | 1.92e-22 | 9.21e-03 | 7.26e-47 | 3.13e-04 | 3.73e-35 | 5.03e-01 | 9.36e-01 | 1.41e-20 | 8.51e-03 |
| 8 | 2.85e-22 | 2.21e-02 | 1.11e-41 | 1.94e-05 | 4.03e-36 | 8.67e-01 | 9.50e-01 | 4.12e-25 | 2.96e-04 |
| 9 | 7.24e-22 | 4.08e-03 | 9.90e-42 | 2.20e-06 | 1.04e-34 | 8.64e-01 | 9.48e-01 | 1.32e-30 | 8.99e-04 |
| 10 | 1.48e-22 | 4.52e-02 | 6.27e-42 | 3.57e-07 | 7.40e-35 | 6.39e-01 | 9.85e-01 | 2.08e-27 | 1.89e-10 |
| 11 | 3.19e-21 | 8.90e-02 | 1.36e-40 | 2.10e-12 | 6.14e-40 | 8.90e-01 | 9.07e-01 | 3.63e-27 | 3.96e-07 |
| 12 | 1.71e-20 | 8.02e-03 | 2.34e-44 | 1.80e-09 | 4.42e-40 | 8.33e-01 | 8.55e-01 | 3.79e-30 | 6.30e-08 |
| 13 | 7.98e-32 | 8.96e-03 | 1.38e-40 | 2.09e-08 | 4.16e-42 | 3.41e-01 | 9.53e-01 | 2.79e-29 | 1.33e-08 |
| 14 | 6.87e-20 | 7.79e-03 | 2.45e-47 | 3.10e-08 | 1.88e-41 | 8.13e-01 | 8.66e-01 | 1.37e-30 | 1.07e-08 |
| 15 | 5.71e-31 | 5.30e-02 | 2.14e-42 | 1.32e-08 | 2.61e-41 | 4.17e-01 | 9.47e-01 | 1.07e-32 | 3.20e-08 |
| 16 | 1.72e-25 | 2.46e-02 | 4.47e-39 | 2.00e-13 | 5.38e-43 | 9.97e-01 | 9.80e-01 | 9.44e-33 | 7.08e-12 |
| 17 | 9.71e-31 | 3.43e-02 | 5.45e-41 | 8.29e-12 | 5.55e-43 | 4.57e-01 | 9.83e-01 | 1.33e-30 | 5.56e-10 |
| 18 | 6.36e-26 | 4.48e-02 | 4.13e-45 | 1.48e-10 | 2.81e-40 | 5.45e-01 | 9.97e-01 | 1.01e-34 | 2.35e-11 |
| 19 | 7.75e-26 | 3.75e-16 | 1.02e-41 | 1.84e-13 | 6.22e-39 | 9.14e-01 | 9.86e-01 | 1.08e-34 | 8.86e-14 |
| 20 | 1.75e-26 | 1.34e-16 | 2.33e-46 | 8.01e-11 | 1.97e-40 | 9.35e-01 | 9.92e-01 | 9.79e-30 | 4.81e-17 |
| 21 | 1.05e-24 | 9.66e-15 | 1.33e-45 | 1.99e-09 | 1.37e-38 | 6.35e-01 | 9.84e-01 | 6.17e-38 | 2.13e-11 |
| 22 | 2.13e-24 | 1.14e-14 | 9.61e-43 | 4.98e-10 | 5.16e-43 | 8.73e-01 | 9.49e-01 | 3.98e-34 | 9.08e-13 |
| 23 | 4.12e-25 | 7.76e-13 | 4.57e-40 | 4.20e-10 | 5.55e-46 | 4.77e-01 | 9.99e-01 | 6.13e-31 | 1.20e-16 |
| 24 | 2.52e-22 | 2.83e-13 | 6.93e-44 | 1.44e-12 | 2.60e-42 | 9.84e-01 | 8.39e-01 | 3.20e-32 | 2.27e-15 |
| 25 | 1.84e-24 | 5.74e-13 | 1.64e-42 | 3.58e-12 | 1.33e-39 | 3.16e-01 | 9.60e-01 | 8.12e-33 | 1.25e-18 |
| 26 | 1.76e-24 | 2.24e-15 | 2.81e-44 | 7.80e-10 | 1.12e-40 | 9.69e-01 | 1.00e+00 | 6.14e-35 | 2.28e-16 |
| 27 | 5.44e-31 | 3.08e-13 | 1.30e-44 | 7.30e-14 | 1.74e-39 | 2.47e-01 | 6.37e-01 | 5.40e-30 | 3.73e-15 |
| 28 | 3.72e-22 | 1.07e-14 | 1.15e-42 | 5.19e-10 | 1.28e-45 | 8.79e-01 | 1.00e+00 | 1.54e-30 | 1.41e-14 |
| 29 | 8.49e-22 | 1.39e-14 | 9.34e-38 | 8.60e-11 | 6.02e-42 | 9.49e-01 | 9.89e-01 | 1.69e-35 | 1.77e-18 |
| 30 | 6.80e-20 | 6.41e-14 | 1.28e-42 | 7.10e-11 | 1.30e-40 | 9.56e-01 | 1.00e+00 | 6.11e-33 | 3.68e-19 |
| 31 | 1.29e-22 | 1.74e-18 | 9.20e-41 | 7.18e-09 | 3.84e-41 | 2.40e-01 | 9.92e-01 | 4.10e-33 | 7.37e-18 |
| 32 | 5.27e-26 | 3.96e-13 | 3.91e-40 | 9.26e-13 | 1.30e-38 | 9.28e-01 | 9.61e-01 | 3.50e-31 | 8.21e-17 |
| 33 | 2.74e-24 | 9.82e-12 | 5.33e-42 | 6.97e-10 | 9.23e-39 | 9.74e-01 | 1.00e+00 | 2.57e-32 | 3.73e-18 |
| 34 | 1.40e-23 | 2.54e-15 | 5.60e-41 | 7.41e-11 | 2.61e-40 | 9.32e-01 | 1.00e+00 | 1.26e-30 | 1.25e-14 |
| 35 | 9.63e-21 | 1.13e-14 | 4.22e-42 | 3.25e-12 | 1.30e-39 | 7.33e-01 | 1.00e+00 | 1.59e-30 | 1.94e-16 |
| 36 | 1.05e-20 | 3.12e-12 | 2.39e-40 | 7.59e-12 | 7.21e-38 | 9.09e-01 | 1.00e+00 | 3.62e-31 | 3.32e-19 |
| 37 | 2.65e-21 | 5.18e-22 | 8.56e-41 | 1.85e-11 | 2.88e-39 | 7.36e-01 | 1.00e+00 | 1.64e-29 | 3.11e-16 |
| 38 | 2.51e-22 | 1.81e-20 | 5.30e-43 | 8.82e-11 | 6.56e-40 | 9.56e-01 | 1.00e+00 | 4.37e-27 | 3.78e-17 |
| 39 | 1.09e-23 | 2.13e-21 | 4.73e-39 | 3.79e-13 | 1.04e-35 | 9.92e-01 | 9.99e-01 | 5.29e-33 | 1.03e-16 |
| 40 | 3.93e-25 | 8.75e-21 | 2.29e-35 | 4.53e-17 | 1.34e-37 | 9.99e-01 | 9.95e-01 | 3.89e-29 | 3.09e-16 |
showPValues(1) # Venues Depth 1
P-Values thresholded at 0.05 for features: Venues Depth 1
| ATM | Adult Boutique | Afghan Restaurant | African Restaurant | Airport | American Restaurant | Animal Shelter | Antique Shop | Aquarium | Art Gallery | Arts & Crafts Store | Asian Restaurant | Athletics & Sports | Auto Dealership | Automotive Shop | BBQ Joint | Baby Store | Bagel Shop | Bakery | Bank | Bar | Beach | Belgian Restaurant | Big Box Store | Bike Shop | Bistro | Boat or Ferry | Bookstore | Bowling Alley | Breakfast Spot | Brewery | Bridal Shop | Bridge | Bubble Tea Shop | Building | Burger Joint | Bus Station | Bus Stop | Business Service | Cafeteria | Café | Cajun / Creole Restaurant | Candy Store | Caribbean Restaurant | Carpet Store | Castle | Cemetery | Chocolate Shop | Clothing Store | Coffee Shop | College Academic Building | College Gym | College Quad | College Rec Center | College Stadium | Comfort Food Restaurant | Comic Shop | Community Center | Concert Hall | Convenience Store | Cosmetics Shop | Costume Shop | Creperie | Deli / Bodega | Department Store | Dessert Shop | Diner | Discount Store | Distribution Center | Dog Run | Donut Shop | Dry Cleaner | Dumpling Restaurant | Eastern European Restaurant | Electronics Store | Escape Room | Event Space | Falafel Restaurant | Farm | Fast Food Restaurant | Field | Fish & Chips Shop | Flea Market | Flower Shop | Food | Food & Drink Shop | Food Court | Food Truck | Fountain | French Restaurant | Fried Chicken Joint | Fruit & Vegetable Store | Furniture / Home Store | Gaming Cafe | Garden | Garden Center | Gas Station | Gastropub | General Entertainment | General Travel | German Restaurant | Gift Shop | Gluten-free Restaurant | Government Building | Greek Restaurant | Halal Restaurant | Harbor / Marina | Hardware Store | Hawaiian Restaurant | Historic Site | Hobby Shop | Hotel | IT Services | Indian Restaurant | Indoor Play Area | Intersection | Irish Pub | Italian Restaurant | Jewelry Store | Jewish Restaurant | Juice Bar | Kitchen Supply Store | Lake | Latin American Restaurant | Laundry Service | Leather Goods Store | Library | Light Rail Station | Lounge | Luggage Store | Market | Massage Studio | Medical Center | Medical Supply Store | Mediterranean Restaurant | Metro Station | Mexican Restaurant | Middle Eastern Restaurant | Miscellaneous Shop | Mobile Phone Shop | Modern European Restaurant | Molecular Gastronomy Restaurant | Movie Theater | Moving Target | Museum | Music Store | Music Venue | Nail Salon | Nightclub | Office | Optical Shop | Other Great Outdoors | Other Nightlife | Other Repair Shop | Outdoor Supply Store | Pakistani Restaurant | Paper / Office Supplies Store | Park | Performing Arts Venue | Pet Store | Pharmacy | Pizza Place | Playground | Plaza | Polish Restaurant | Pool | Pool Hall | Portuguese Restaurant | Poutine Place | Racetrack | Record Shop | Recreation Center | Rental Car Location | Rental Service | Residential Building (Apartment / Condo) | Restaurant | River | Rock Climbing Spot | Roof Deck | Salad Place | Salon / Barbershop | Sandwich Place | Scenic Lookout | School | Sculpture Garden | Seafood Restaurant | Shopping Mall | Shopping Plaza | Smoke Shop | Smoothie Shop | Snack Place | Soup Place | Spa | Spanish Restaurant | Spiritual Center | Sporting Goods Shop | Sri Lankan Restaurant | Stadium | States & Municipalities | Stationery Store | Steakhouse | Strip Club | Supplement Shop | Swiss Restaurant | Tailor Shop | Tanning Salon | Tea Room | Theme Park | Theme Restaurant | Thrift / Vintage Store | Toy / Game Store | Trail | Train Station | Tram Station | Transportation Service | Tree | Turkish Restaurant | Vegetarian / Vegan Restaurant | Video Game Store | Video Store | Warehouse Store | Waste Facility | Wings Joint | Zoo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2 | 7.83e-01 | 8.36e-01 | 8.02e-01 | 6.50e-01 | 8.36e-01 | 3.05e-01 | 8.36e-01 | 7.27e-01 | 8.36e-01 | 5.27e-01 | 6.41e-01 | 3.64e-02 | 1.79e-01 | 8.36e-01 | 7.17e-01 | 4.76e-01 | 7.19e-01 | 5.67e-01 | 6.53e-02 | 1.10e-01 | 1.27e-01 | 7.75e-01 | 7.69e-01 | 7.86e-01 | 7.34e-01 | 7.78e-01 | 7.34e-01 | 4.73e-01 | 8.36e-01 | 3.09e-01 | 5.29e-01 | 7.45e-01 | 8.36e-01 | 4.95e-01 | 7.91e-01 | 2.59e-01 | 5.33e-01 | 6.57e-01 | 7.66e-01 | 8.36e-01 | 8.49e-02 | 8.36e-01 | 4.16e-07 | 3.45e-01 | 8.36e-01 | 8.36e-01 | 8.36e-01 | 6.85e-01 | 5.20e-01 | 1.67e-03 | 8.36e-01 | 7.24e-01 | 8.36e-01 | 8.05e-01 | 8.36e-01 | 5.49e-01 | 7.19e-01 | 8.36e-01 | 6.43e-01 | 4.09e-01 | 5.28e-01 | 8.36e-01 | 6.81e-01 | 6.96e-01 | 5.23e-01 | 2.00e-01 | 3.60e-01 | 3.78e-01 | 6.88e-01 | 7.76e-01 | 6.82e-01 | 8.36e-01 | 7.65e-01 | 6.79e-01 | 3.98e-01 | 8.36e-01 | 7.21e-01 | 6.14e-01 | 8.36e-01 | 1.62e-01 | 8.36e-01 | 5.41e-01 | 6.86e-01 | 6.01e-01 | 7.69e-01 | 4.17e-01 | 6.17e-01 | 6.15e-07 | 7.73e-01 | 4.80e-01 | 3.89e-01 | 7.69e-01 | 5.31e-01 | 7.69e-01 | 7.39e-07 | 8.36e-01 | 3.19e-01 | 3.69e-01 | 7.69e-01 | 8.36e-01 | 7.24e-01 | 5.17e-01 | 8.36e-01 | 7.69e-01 | 4.16e-01 | 8.36e-01 | 8.36e-01 | 6.67e-01 | 7.71e-01 | 6.58e-01 | 6.57e-01 | 4.84e-01 | 8.36e-01 | 3.67e-01 | 8.36e-01 | 1.15e-02 | 7.21e-01 | 1.54e-01 | 6.16e-01 | 8.36e-01 | 5.23e-01 | 8.36e-01 | 7.75e-01 | 5.50e-01 | 8.36e-01 | 8.36e-01 | 8.36e-01 | 6.50e-01 | 5.38e-01 | 8.36e-01 | 7.71e-01 | 8.36e-01 | 6.47e-01 | 8.36e-01 | 3.70e-01 | 6.24e-01 | 2.18e-01 | 3.46e-01 | 7.19e-01 | 3.10e-06 | 6.89e-01 | 8.36e-01 | 4.74e-01 | 8.36e-01 | 7.00e-01 | 8.36e-01 | 5.51e-01 | 8.36e-01 | 7.52e-01 | 7.37e-01 | 7.05e-01 | 6.98e-01 | 8.36e-01 | 8.36e-01 | 8.36e-01 | 7.80e-01 | 5.75e-01 | 3.58e-10 | 3.91e-01 | 4.19e-01 | 1.16e-01 | 3.93e-02 | 2.20e-13 | 6.31e-01 | 8.36e-01 | 6.01e-01 | 7.18e-01 | 7.33e-01 | 8.36e-01 | 8.36e-01 | 6.46e-01 | 8.09e-01 | 7.37e-01 | 8.36e-01 | 7.69e-01 | 6.10e-02 | 8.36e-01 | 8.02e-01 | 8.36e-01 | 6.52e-01 | 6.26e-01 | 4.97e-02 | 7.69e-01 | 7.72e-01 | 8.36e-01 | 4.06e-01 | 4.48e-01 | 8.36e-01 | 7.19e-01 | 5.81e-01 | 7.21e-01 | 7.72e-01 | 4.17e-01 | 6.66e-01 | 7.76e-01 | 5.62e-01 | 8.36e-01 | 7.13e-01 | 7.24e-01 | 8.36e-01 | 4.96e-01 | 8.36e-01 | 7.34e-01 | 8.36e-01 | 7.73e-01 | 7.71e-01 | 6.04e-01 | 8.36e-01 | 8.36e-01 | 6.74e-01 | 4.61e-01 | 4.58e-03 | 7.03e-01 | 8.36e-01 | 8.36e-01 | 8.36e-01 | 7.69e-01 | 4.14e-01 | 6.84e-01 | 8.36e-01 | 7.70e-01 | 8.36e-01 | 5.38e-01 | 8.36e-01 |
| 3 | 9.34e-01 | 9.62e-01 | 9.45e-01 | 8.31e-01 | 9.62e-01 | 3.87e-01 | 9.62e-01 | 8.96e-01 | 9.62e-01 | 6.98e-01 | 8.22e-01 | 1.80e-02 | 4.43e-05 | 9.62e-01 | 8.89e-01 | 6.33e-01 | 8.90e-01 | 7.44e-01 | 4.49e-02 | 4.86e-01 | 1.20e-01 | 9.29e-01 | 9.25e-01 | 9.36e-01 | 9.01e-01 | 9.31e-01 | 9.01e-01 | 1.09e-01 | 9.62e-01 | 6.19e-01 | 6.99e-01 | 9.09e-01 | 9.62e-01 | 6.58e-01 | 9.38e-01 | 5.72e-01 | 1.28e-01 | 8.38e-01 | 9.23e-01 | 9.62e-01 | 2.82e-01 | 9.62e-01 | 3.81e-04 | 4.47e-01 | 3.38e-04 | 9.62e-01 | 9.62e-01 | 8.62e-01 | 6.88e-01 | 3.29e-03 | 9.62e-01 | 8.94e-01 | 3.38e-04 | 9.47e-01 | 9.62e-01 | 7.23e-01 | 8.90e-01 | 9.62e-01 | 8.24e-01 | 5.40e-01 | 6.99e-01 | 9.62e-01 | 8.59e-01 | 8.72e-01 | 6.93e-01 | 2.25e-01 | 6.98e-01 | 4.95e-01 | 8.65e-01 | 9.30e-01 | 8.60e-01 | 9.62e-01 | 9.23e-01 | 8.57e-01 | 5.24e-01 | 9.62e-01 | 8.91e-01 | 7.95e-01 | 9.62e-01 | 1.69e-01 | 9.62e-01 | 7.15e-01 | 8.63e-01 | 7.81e-01 | 9.25e-01 | 1.01e-01 | 7.98e-01 | 5.09e-04 | 9.28e-01 | 6.38e-01 | 5.12e-01 | 9.25e-01 | 7.03e-01 | 9.25e-01 | 2.48e-69 | 9.62e-01 | 4.07e-01 | 4.83e-01 | 1.03e-03 | 9.62e-01 | 8.94e-01 | 6.85e-01 | 9.62e-01 | 9.25e-01 | 5.51e-01 | 9.62e-01 | 9.62e-01 | 8.47e-01 | 9.26e-01 | 8.38e-01 | 8.38e-01 | 6.43e-01 | 9.62e-01 | 4.80e-01 | 9.62e-01 | 2.32e-01 | 8.92e-01 | 7.29e-01 | 7.97e-01 | 9.62e-01 | 6.93e-01 | 9.62e-01 | 9.29e-01 | 7.25e-01 | 9.62e-01 | 9.62e-01 | 9.62e-01 | 8.31e-01 | 7.10e-01 | 9.62e-01 | 9.26e-01 | 9.62e-01 | 8.28e-01 | 9.62e-01 | 4.85e-01 | 8.06e-01 | 2.54e-01 | 4.49e-01 | 8.90e-01 | 1.39e-03 | 8.66e-01 | 9.62e-01 | 6.29e-01 | 9.62e-01 | 8.75e-01 | 9.62e-01 | 7.26e-01 | 9.62e-01 | 9.14e-01 | 9.03e-01 | 8.79e-01 | 8.73e-01 | 9.62e-01 | 9.62e-01 | 9.62e-01 | 9.32e-01 | 7.53e-01 | 3.62e-09 | 5.15e-01 | 5.55e-01 | 4.61e-01 | 1.29e-01 | 1.03e-51 | 8.13e-01 | 9.62e-01 | 7.81e-01 | 8.89e-01 | 9.00e-01 | 9.62e-01 | 9.62e-01 | 8.27e-01 | 4.62e-04 | 9.04e-01 | 9.62e-01 | 9.26e-01 | 5.83e-01 | 9.62e-01 | 9.45e-01 | 9.62e-01 | 8.32e-01 | 8.08e-01 | 2.94e-02 | 9.25e-01 | 9.27e-01 | 9.62e-01 | 5.36e-01 | 5.95e-01 | 9.62e-01 | 8.90e-01 | 7.60e-01 | 8.91e-01 | 9.27e-01 | 5.52e-01 | 8.46e-01 | 9.30e-01 | 7.39e-01 | 9.62e-01 | 8.85e-01 | 1.77e-03 | 9.62e-01 | 6.58e-01 | 9.62e-01 | 9.01e-01 | 9.62e-01 | 9.28e-01 | 9.26e-01 | 7.85e-01 | 9.62e-01 | 9.62e-01 | 8.53e-01 | 6.13e-01 | 2.60e-04 | 8.77e-01 | 9.62e-01 | 9.62e-01 | 9.62e-01 | 9.25e-01 | 5.48e-01 | 8.62e-01 | 9.62e-01 | 9.26e-01 | 9.62e-01 | 7.11e-01 | 9.62e-01 |
| 4 | 8.34e-01 | 4.47e-01 | 9.84e-01 | 9.54e-01 | 9.21e-01 | 2.52e-01 | 9.21e-01 | 7.06e-01 | 9.21e-01 | 8.94e-01 | 9.08e-01 | 7.41e-21 | 3.91e-04 | 9.21e-01 | 9.70e-01 | 2.75e-01 | 9.78e-01 | 9.19e-01 | 1.17e-01 | 6.75e-01 | 2.32e-01 | 8.18e-01 | 8.04e-01 | 9.90e-01 | 9.77e-01 | 8.24e-01 | 7.23e-01 | 6.38e-01 | 9.21e-01 | 6.14e-01 | 2.02e-01 | 8.00e-01 | 9.21e-01 | 7.64e-02 | 9.90e-01 | 5.54e-01 | 8.98e-01 | 9.42e-01 | 7.98e-01 | 4.47e-01 | 5.64e-01 | 9.21e-01 | 2.23e-04 | 2.80e-01 | 1.99e-04 | 9.21e-01 | 9.21e-01 | 5.95e-01 | 5.99e-01 | 7.32e-04 | 4.47e-01 | 9.16e-01 | 9.21e-01 | 9.81e-01 | 4.47e-01 | 7.31e-01 | 9.78e-01 | 9.21e-01 | 7.70e-01 | 4.88e-01 | 8.91e-01 | 9.21e-01 | 6.85e-01 | 8.37e-01 | 2.76e-01 | 1.17e-01 | 7.49e-01 | 5.74e-01 | 9.36e-01 | 9.62e-01 | 5.87e-01 | 9.21e-01 | 9.80e-01 | 7.61e-01 | 7.79e-01 | 4.47e-01 | 9.45e-01 | 9.11e-01 | 4.47e-01 | 1.07e-01 | 9.21e-01 | 4.68e-01 | 8.61e-01 | 9.33e-01 | 8.05e-01 | 9.02e-02 | 6.38e-01 | 3.00e-04 | 8.12e-01 | 4.62e-01 | 7.68e-01 | 8.04e-01 | 5.19e-01 | 8.04e-01 | 9.93e-68 | 9.21e-01 | 6.69e-01 | 6.85e-01 | 6.08e-04 | 4.47e-01 | 3.58e-01 | 8.22e-01 | 4.47e-01 | 9.19e-01 | 5.74e-01 | 4.47e-01 | 9.21e-01 | 8.92e-01 | 8.24e-01 | 8.58e-01 | 7.24e-01 | 6.93e-01 | 9.21e-01 | 7.39e-01 | 9.21e-01 | 2.32e-01 | 6.92e-01 | 6.73e-01 | 9.43e-01 | 9.21e-01 | 5.85e-01 | 9.21e-01 | 9.78e-01 | 7.21e-01 | 9.21e-01 | 9.21e-01 | 4.47e-01 | 9.49e-01 | 7.50e-01 | 9.21e-01 | 8.23e-01 | 4.47e-01 | 9.03e-01 | 9.21e-01 | 5.03e-01 | 7.70e-01 | 1.71e-01 | 5.26e-01 | 9.78e-01 | 8.80e-04 | 8.74e-01 | 9.21e-01 | 8.15e-01 | 4.47e-01 | 9.73e-01 | 4.47e-01 | 8.70e-01 | 9.21e-01 | 8.94e-01 | 7.30e-01 | 6.50e-01 | 1.14e-01 | 9.21e-01 | 9.21e-01 | 9.21e-01 | 6.96e-01 | 1.51e-01 | 6.65e-09 | 5.80e-01 | 1.04e-01 | 5.63e-01 | 3.67e-01 | 1.01e-52 | 9.49e-01 | 4.47e-01 | 5.61e-01 | 5.46e-01 | 7.20e-01 | 4.47e-01 | 9.21e-01 | 8.66e-01 | 2.75e-04 | 2.49e-01 | 9.21e-01 | 8.06e-01 | 1.41e-01 | 9.21e-01 | 9.84e-01 | 9.21e-01 | 5.61e-02 | 8.99e-01 | 1.02e-01 | 8.04e-01 | 9.65e-01 | 4.47e-01 | 7.86e-01 | 5.21e-01 | 9.21e-01 | 4.00e-02 | 7.43e-01 | 9.38e-01 | 8.10e-01 | 8.65e-02 | 5.44e-01 | 8.19e-01 | 6.11e-01 | 4.47e-01 | 8.81e-01 | 1.18e-03 | 9.21e-01 | 7.36e-01 | 4.47e-01 | 7.23e-01 | 4.47e-01 | 8.12e-01 | 9.52e-01 | 9.18e-01 | 9.21e-01 | 4.47e-01 | 5.66e-01 | 1.80e-01 | 5.30e-02 | 8.96e-01 | 9.21e-01 | 9.21e-01 | 9.21e-01 | 8.05e-01 | 5.68e-01 | 8.32e-01 | 9.21e-01 | 9.46e-01 | 9.21e-01 | 8.97e-01 | 9.21e-01 |
| 5 | 4.79e-01 | 8.04e-01 | 8.27e-01 | 2.65e-01 | 5.66e-01 | 1.27e-01 | 8.04e-01 | 3.18e-01 | 8.04e-01 | 5.81e-02 | 8.59e-01 | 5.97e-16 | 6.38e-02 | 8.04e-01 | 9.92e-01 | 4.27e-01 | 2.84e-01 | 2.61e-01 | 1.37e-01 | 8.17e-02 | 9.49e-09 | 5.37e-01 | 5.06e-01 | 8.41e-01 | 9.63e-01 | 8.48e-01 | 8.79e-01 | 3.07e-02 | 8.04e-01 | 7.21e-01 | 4.49e-01 | 8.68e-01 | 7.44e-01 | 2.54e-01 | 8.61e-01 | 2.72e-01 | 8.76e-01 | 6.39e-01 | 7.27e-01 | 8.04e-01 | 2.11e-04 | 8.04e-01 | 2.95e-07 | 2.95e-01 | 8.04e-01 | 8.04e-01 | 7.44e-01 | 1.69e-01 | 4.37e-02 | 2.23e-03 | 8.04e-01 | 3.05e-01 | 8.04e-01 | 8.20e-01 | 5.66e-01 | 5.52e-03 | 2.85e-01 | 7.44e-01 | 7.53e-02 | 3.04e-01 | 5.15e-02 | 7.44e-01 | 1.58e-01 | 7.81e-01 | 4.28e-01 | 2.47e-01 | 8.41e-01 | 9.49e-02 | 1.78e-01 | 8.60e-01 | 6.69e-01 | 8.04e-01 | 7.98e-01 | 8.07e-01 | 2.07e-01 | 8.04e-01 | 7.81e-01 | 4.11e-01 | 8.04e-01 | 2.08e-02 | 7.44e-01 | 4.71e-01 | 7.21e-01 | 8.28e-01 | 5.07e-01 | 4.31e-04 | 1.23e-01 | 4.59e-07 | 5.24e-01 | 2.45e-01 | 1.71e-01 | 5.06e-01 | 9.63e-01 | 9.36e-01 | 1.64e-66 | 8.04e-01 | 2.74e-02 | 8.74e-03 | 8.87e-01 | 8.04e-01 | 4.15e-01 | 1.65e-03 | 8.04e-01 | 5.07e-01 | 8.00e-01 | 5.66e-01 | 8.04e-01 | 9.94e-01 | 8.23e-01 | 1.03e-01 | 9.72e-01 | 4.17e-01 | 8.04e-01 | 8.13e-01 | 7.44e-01 | 1.11e-03 | 8.70e-01 | 2.18e-01 | 4.06e-02 | 8.04e-01 | 5.84e-01 | 8.04e-01 | 5.38e-01 | 9.22e-01 | 7.44e-01 | 8.04e-01 | 8.04e-01 | 6.93e-01 | 1.64e-01 | 8.04e-01 | 5.15e-01 | 5.66e-01 | 7.89e-01 | 7.44e-01 | 6.50e-01 | 1.92e-01 | 9.40e-03 | 6.57e-01 | 2.88e-01 | 3.25e-06 | 6.44e-01 | 8.04e-01 | 3.65e-01 | 5.66e-01 | 6.88e-01 | 5.66e-01 | 6.03e-03 | 8.04e-01 | 8.66e-01 | 8.78e-01 | 7.11e-01 | 3.74e-01 | 8.04e-01 | 5.66e-01 | 8.04e-01 | 7.53e-01 | 4.07e-01 | 1.01e-15 | 1.19e-02 | 2.53e-01 | 1.12e-03 | 5.21e-07 | 1.06e-53 | 7.25e-01 | 8.04e-01 | 6.25e-01 | 5.65e-01 | 5.61e-01 | 8.04e-01 | 7.44e-01 | 4.74e-01 | 8.05e-01 | 3.14e-01 | 7.44e-01 | 8.38e-01 | 1.07e-02 | 8.04e-01 | 8.28e-01 | 8.04e-01 | 5.90e-01 | 3.51e-01 | 8.25e-03 | 5.06e-01 | 5.23e-01 | 8.04e-01 | 7.10e-01 | 2.94e-01 | 7.44e-01 | 2.84e-01 | 9.05e-01 | 2.93e-01 | 5.19e-01 | 3.37e-01 | 7.09e-01 | 5.41e-01 | 8.01e-01 | 5.66e-01 | 5.18e-01 | 8.63e-01 | 8.04e-01 | 8.04e-03 | 8.04e-01 | 8.94e-01 | 5.66e-01 | 5.24e-01 | 5.15e-01 | 4.13e-01 | 8.04e-01 | 8.04e-01 | 4.46e-01 | 8.81e-02 | 7.66e-03 | 7.29e-01 | 8.04e-01 | 7.44e-01 | 8.04e-01 | 7.67e-01 | 1.75e-03 | 1.68e-01 | 7.44e-01 | 8.45e-01 | 7.44e-01 | 9.84e-01 | 7.44e-01 |
| 6 | 6.75e-01 | 8.90e-01 | 9.32e-01 | 3.61e-01 | 7.10e-01 | 1.21e-01 | 8.90e-01 | 4.27e-01 | 8.90e-01 | 8.74e-02 | 9.23e-01 | 4.44e-16 | 5.48e-04 | 8.90e-01 | 9.99e-01 | 5.58e-01 | 3.88e-01 | 3.53e-01 | 1.94e-01 | 2.29e-01 | 7.18e-09 | 6.61e-01 | 6.30e-01 | 9.39e-01 | 9.92e-01 | 9.43e-01 | 9.45e-01 | 3.82e-01 | 8.90e-01 | 8.73e-01 | 7.47e-01 | 9.43e-01 | 8.80e-01 | 3.37e-01 | 9.50e-01 | 3.48e-01 | 3.14e-01 | 8.16e-01 | 8.42e-01 | 8.90e-01 | 1.35e-07 | 8.90e-01 | 8.97e-01 | 4.58e-01 | 0.00e+00 | 8.90e-01 | 8.90e-01 | 2.44e-01 | 6.70e-02 | 1.33e-02 | 8.90e-01 | 4.12e-01 | 8.80e-01 | 9.28e-01 | 7.10e-01 | 9.24e-03 | 3.89e-01 | 8.80e-01 | 1.15e-01 | 6.46e-01 | 7.89e-02 | 8.80e-01 | 2.29e-01 | 9.05e-01 | 5.59e-01 | 4.60e-01 | 8.96e-01 | 3.02e-01 | 2.55e-01 | 9.49e-01 | 7.84e-01 | 8.90e-01 | 8.83e-01 | 9.11e-01 | 3.13e-01 | 8.90e-01 | 8.73e-01 | 5.31e-01 | 8.90e-01 | 7.58e-02 | 8.80e-01 | 6.39e-01 | 8.80e-01 | 9.00e-01 | 6.31e-01 | 5.69e-04 | 2.02e-01 | 1.40e-75 | 6.48e-01 | 3.25e-01 | 5.43e-01 | 6.30e-01 | 9.92e-01 | 9.79e-01 | 4.91e-65 | 8.90e-01 | 1.47e-01 | 2.38e-03 | 8.50e-48 | 8.90e-01 | 5.59e-01 | 2.79e-03 | 8.90e-01 | 6.31e-01 | 8.33e-01 | 7.10e-01 | 8.90e-01 | 9.99e-01 | 9.08e-01 | 1.54e-01 | 9.94e-01 | 6.41e-01 | 8.90e-01 | 9.53e-01 | 8.80e-01 | 7.24e-02 | 9.42e-01 | 1.53e-01 | 6.43e-02 | 8.90e-01 | 7.04e-01 | 8.90e-01 | 6.62e-01 | 9.85e-01 | 8.80e-01 | 8.90e-01 | 8.90e-01 | 8.26e-01 | 2.31e-01 | 8.90e-01 | 6.40e-01 | 7.10e-01 | 9.12e-01 | 8.80e-01 | 7.62e-01 | 2.70e-01 | 9.36e-03 | 8.53e-01 | 3.92e-01 | 1.09e-41 | 7.57e-01 | 8.90e-01 | 4.62e-01 | 7.10e-01 | 7.95e-01 | 7.10e-01 | 1.01e-02 | 8.90e-01 | 9.51e-01 | 9.56e-01 | 8.23e-01 | 5.15e-01 | 8.90e-01 | 7.10e-01 | 8.90e-01 | 8.61e-01 | 5.58e-01 | 1.30e-05 | 1.86e-02 | 3.47e-01 | 2.00e-02 | 3.32e-04 | 7.36e-42 | 8.23e-01 | 8.90e-01 | 7.58e-01 | 7.05e-01 | 7.54e-01 | 8.90e-01 | 8.80e-01 | 5.92e-01 | 5.11e-79 | 4.50e-01 | 8.80e-01 | 9.18e-01 | 1.11e-01 | 8.90e-01 | 9.32e-01 | 8.90e-01 | 7.11e-01 | 4.59e-01 | 7.03e-02 | 6.30e-01 | 6.47e-01 | 8.90e-01 | 5.41e-01 | 4.79e-01 | 8.80e-01 | 3.88e-01 | 9.75e-01 | 3.98e-01 | 6.44e-01 | 4.74e-01 | 8.23e-01 | 6.65e-01 | 9.03e-01 | 7.10e-01 | 7.21e-01 | 9.48e-01 | 8.90e-01 | 1.29e-02 | 8.90e-01 | 9.60e-01 | 7.10e-01 | 6.48e-01 | 6.39e-01 | 5.35e-01 | 8.90e-01 | 8.90e-01 | 6.56e-01 | 3.59e-02 | 1.95e-01 | 8.83e-01 | 8.90e-01 | 8.80e-01 | 8.90e-01 | 8.77e-01 | 2.77e-03 | 2.42e-01 | 8.80e-01 | 9.37e-01 | 8.80e-01 | 9.98e-01 | 8.80e-01 |
| 7 | 6.76e-01 | 9.87e-01 | 9.65e-01 | 8.41e-01 | 9.87e-01 | 3.59e-01 | 9.87e-01 | 8.37e-01 | 9.87e-01 | 5.18e-01 | 9.97e-01 | 9.57e-18 | 2.41e-01 | 9.87e-01 | 9.85e-01 | 8.70e-01 | 8.15e-01 | 5.05e-01 | 2.98e-02 | 1.23e-01 | 1.36e-05 | 9.34e-01 | 9.24e-01 | 9.67e-01 | 9.64e-01 | 9.56e-01 | 9.96e-01 | 5.23e-01 | 9.87e-01 | 9.73e-01 | 5.30e-01 | 8.07e-01 | 9.87e-01 | 4.64e-01 | 9.61e-01 | 4.24e-01 | 9.87e-01 | 8.52e-01 | 9.19e-01 | 9.87e-01 | 4.57e-02 | 9.87e-01 | 9.11e-01 | 4.63e-02 | 9.87e-01 | 9.87e-01 | 9.87e-01 | 7.09e-01 | 4.14e-01 | 2.33e-01 | 9.87e-01 | 8.29e-01 | 9.87e-01 | 9.39e-01 | 9.87e-01 | 2.10e-01 | 8.16e-01 | 8.96e-01 | 5.51e-01 | 1.59e-15 | 5.76e-01 | 9.87e-01 | 6.95e-01 | 3.92e-33 | 5.58e-01 | 7.25e-01 | 8.45e-01 | 3.25e-01 | 7.19e-01 | 9.62e-01 | 9.81e-01 | 9.87e-01 | 9.83e-01 | 9.77e-01 | 5.09e-01 | 9.87e-01 | 9.06e-01 | 9.31e-01 | 9.87e-01 | 2.74e-01 | 0.00e+00 | 8.83e-01 | 1.00e+00 | 9.77e-01 | 9.24e-01 | 3.67e-03 | 5.05e-01 | 5.13e-74 | 9.30e-01 | 2.30e-01 | 6.01e-01 | 9.24e-01 | 9.55e-01 | 9.96e-01 | 9.68e-64 | 9.87e-01 | 7.77e-01 | 2.77e-03 | 9.72e-01 | 9.87e-01 | 7.67e-01 | 1.29e-01 | 9.87e-01 | 9.24e-01 | 9.46e-01 | 9.87e-01 | 9.87e-01 | 6.45e-01 | 8.39e-01 | 6.10e-01 | 6.51e-13 | 8.25e-01 | 9.87e-01 | 9.53e-01 | 9.87e-01 | 3.26e-01 | 9.94e-01 | 3.36e-01 | 4.46e-01 | 9.87e-01 | 9.89e-01 | 9.87e-01 | 9.34e-01 | 9.42e-01 | 8.96e-01 | 9.87e-01 | 9.87e-01 | 1.00e+00 | 5.99e-01 | 9.87e-01 | 9.27e-01 | 9.87e-01 | 9.80e-01 | 8.96e-01 | 8.41e-01 | 7.86e-01 | 3.70e-01 | 5.46e-01 | 8.17e-01 | 9.51e-41 | 7.23e-01 | 9.87e-01 | 3.76e-01 | 5.03e-01 | 9.31e-01 | 5.03e-01 | 2.17e-01 | 9.87e-01 | 9.70e-01 | 8.61e-01 | 7.76e-01 | 2.19e-01 | 9.87e-01 | 9.87e-01 | 9.87e-01 | 7.22e-01 | 2.77e-06 | 7.34e-07 | 2.66e-01 | 5.39e-01 | 1.46e-04 | 2.22e-04 | 2.36e-41 | 9.94e-01 | 9.87e-01 | 2.88e-01 | 4.61e-01 | 7.49e-01 | 9.87e-01 | 8.96e-01 | 8.66e-01 | 9.90e-01 | 1.68e-01 | 8.96e-01 | 9.93e-01 | 6.08e-03 | 9.87e-01 | 9.65e-01 | 9.87e-01 | 8.67e-01 | 4.87e-01 | 2.29e-01 | 9.24e-01 | 9.29e-01 | 9.87e-01 | 9.20e-01 | 1.96e-01 | 8.96e-01 | 8.15e-01 | 9.99e-01 | 8.21e-01 | 9.28e-01 | 8.51e-02 | 9.83e-01 | 9.35e-01 | 9.94e-01 | 5.03e-01 | 2.62e-41 | 9.78e-01 | 9.87e-01 | 2.75e-01 | 9.87e-01 | 9.90e-01 | 9.87e-01 | 9.30e-01 | 9.27e-01 | 9.33e-01 | 9.87e-01 | 9.87e-01 | 8.24e-01 | 4.80e-01 | 1.52e-05 | 8.55e-01 | 9.87e-01 | 8.96e-01 | 9.87e-01 | 9.24e-01 | 3.62e-02 | 7.07e-01 | 8.96e-01 | 9.46e-01 | 9.87e-01 | 9.99e-01 | 0.00e+00 |
| 8 | 9.92e-01 | 9.30e-01 | 1.00e+00 | 9.98e-01 | 9.99e-01 | 7.45e-01 | 9.99e-01 | 9.66e-01 | 9.99e-01 | 9.77e-01 | 9.97e-01 | 2.12e-18 | 2.58e-04 | 9.30e-01 | 1.00e+00 | 9.07e-01 | 9.69e-01 | 9.80e-01 | 4.99e-01 | 3.52e-01 | 3.62e-01 | 9.90e-01 | 9.99e-01 | 8.33e-02 | 9.96e-01 | 4.91e-02 | 9.71e-01 | 9.52e-01 | 9.99e-01 | 8.32e-01 | 7.37e-01 | 9.95e-01 | 9.99e-01 | 4.90e-01 | 1.00e+00 | 7.00e-01 | 9.97e-01 | 9.98e-01 | 9.87e-01 | 9.30e-01 | 1.79e-04 | 9.99e-01 | 9.14e-11 | 1.87e-01 | 0.00e+00 | 9.99e-01 | 9.99e-01 | 9.24e-01 | 8.73e-01 | 3.15e-06 | 9.30e-01 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 9.30e-01 | 9.91e-01 | 1.00e+00 | 9.99e-01 | 7.85e-01 | 8.59e-01 | 9.86e-01 | 9.99e-01 | 9.84e-01 | 9.87e-01 | 4.81e-01 | 2.96e-01 | 9.07e-01 | 2.92e-01 | 9.97e-01 | 9.99e-01 | 9.98e-01 | 9.99e-01 | 8.40e-01 | 6.25e-01 | 9.24e-01 | 9.30e-01 | 9.99e-01 | 9.92e-01 | 9.30e-01 | 7.12e-02 | 9.99e-01 | 9.72e-01 | 9.90e-01 | 9.96e-01 | 9.88e-01 | 3.45e-01 | 9.73e-01 | 1.65e-72 | 9.89e-01 | 7.62e-01 | 9.91e-01 | 9.88e-01 | 8.38e-01 | 9.99e-01 | 4.24e-62 | 9.99e-01 | 7.39e-01 | 9.07e-01 | 1.02e-45 | 9.30e-01 | 8.87e-01 | 9.77e-01 | 9.30e-01 | 9.99e-01 | 8.06e-01 | 9.30e-01 | 9.99e-01 | 9.98e-01 | 9.97e-01 | 9.88e-01 | 4.63e-12 | 9.07e-01 | 9.99e-01 | 9.61e-01 | 9.99e-01 | 5.63e-03 | 9.62e-01 | 2.43e-01 | 9.97e-01 | 9.99e-01 | 1.61e-02 | 9.99e-01 | 1.00e+00 | 7.86e-01 | 9.99e-01 | 9.99e-01 | 9.30e-01 | 9.97e-01 | 9.81e-01 | 9.99e-01 | 9.97e-01 | 9.99e-01 | 9.98e-01 | 9.99e-01 | 8.73e-01 | 9.67e-01 | 5.02e-01 | 8.79e-01 | 1.00e+00 | 7.19e-42 | 9.98e-01 | 9.99e-01 | 9.77e-01 | 9.30e-01 | 1.00e+00 | 9.30e-01 | 9.91e-01 | 9.99e-01 | 6.57e-02 | 9.73e-01 | 9.95e-01 | 6.01e-01 | 9.99e-01 | 9.99e-01 | 9.99e-01 | 9.87e-01 | 2.90e-07 | 3.11e-15 | 8.58e-01 | 3.89e-01 | 3.83e-02 | 7.99e-02 | 5.92e-62 | 9.98e-01 | 9.30e-01 | 8.48e-01 | 9.61e-01 | 9.70e-01 | 9.30e-01 | 9.99e-01 | 8.08e-01 | 7.47e-76 | 8.03e-01 | 9.99e-01 | 9.88e-01 | 1.58e-01 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 4.33e-01 | 9.97e-01 | 1.23e-01 | 9.88e-01 | 6.73e-01 | 9.30e-01 | 9.44e-01 | 9.30e-01 | 9.99e-01 | 3.60e-01 | 9.92e-01 | 9.99e-01 | 9.89e-01 | 2.53e-02 | 8.97e-01 | 9.90e-01 | 7.84e-01 | 9.30e-01 | 9.93e-01 | 9.88e-01 | 9.99e-01 | 9.73e-01 | 9.30e-01 | 9.71e-01 | 9.30e-01 | 9.89e-01 | 1.00e+00 | 9.78e-01 | 9.99e-01 | 9.30e-01 | 9.82e-01 | 8.58e-01 | 1.35e-04 | 9.94e-01 | 9.99e-01 | 9.99e-01 | 9.99e-01 | 9.88e-01 | 9.51e-01 | 9.96e-01 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 9.87e-01 | 0.00e+00 |
| 9 | 8.78e-01 | 9.78e-01 | 9.89e-01 | 6.69e-01 | 3.83e-01 | 1.88e-01 | 9.78e-01 | 6.32e-01 | 9.78e-01 | 1.70e-01 | 9.44e-01 | 1.59e-17 | 8.70e-02 | 9.78e-01 | 8.98e-01 | 7.63e-01 | 9.80e-01 | 5.30e-01 | 1.24e-02 | 4.90e-03 | 3.47e-11 | 8.52e-01 | 8.28e-01 | 5.59e-02 | 9.91e-01 | 4.61e-01 | 9.87e-01 | 4.27e-02 | 9.78e-01 | 7.13e-01 | 5.86e-01 | 8.48e-01 | 9.78e-01 | 7.28e-02 | 9.93e-01 | 4.28e-01 | 4.15e-01 | 8.55e-01 | 5.39e-01 | 9.78e-01 | 1.03e-02 | 9.78e-01 | 1.55e-02 | 5.42e-02 | 9.78e-01 | 9.78e-01 | 9.78e-01 | 2.18e-06 | 3.99e-21 | 1.68e-12 | 9.78e-01 | 6.15e-01 | 3.83e-01 | 9.55e-01 | 9.78e-01 | 1.32e-01 | 8.36e-05 | 9.78e-01 | 1.95e-01 | 4.47e-03 | 4.45e-07 | 9.78e-01 | 3.74e-01 | 6.28e-02 | 7.51e-01 | 6.13e-01 | 5.98e-01 | 3.80e-01 | 1.11e-04 | 9.92e-01 | 9.04e-01 | 9.78e-01 | 9.59e-01 | 8.99e-01 | 8.32e-01 | 9.78e-01 | 8.91e-01 | 8.11e-01 | 9.78e-01 | 2.91e-02 | 1.42e-02 | 1.31e-01 | 9.56e-01 | 8.71e-01 | 8.29e-01 | 8.01e-03 | 5.51e-02 | 2.49e-71 | 8.42e-01 | 1.30e-01 | 1.43e-01 | 8.28e-01 | 8.33e-01 | 9.97e-01 | 4.33e-61 | 9.78e-01 | 1.56e-02 | 8.08e-03 | 9.21e-01 | 9.78e-01 | 8.14e-01 | 2.86e-01 | 9.78e-01 | 8.29e-01 | 8.85e-01 | 9.78e-01 | 9.78e-01 | 7.94e-01 | 1.78e-01 | 3.96e-01 | 9.90e-12 | 5.88e-01 | 9.78e-01 | 8.84e-01 | 9.78e-01 | 8.51e-02 | 9.85e-01 | 2.66e-01 | 1.76e-08 | 9.78e-01 | 5.29e-02 | 1.60e-03 | 5.62e-01 | 4.97e-01 | 9.78e-01 | 1.60e-03 | 9.78e-01 | 8.22e-01 | 3.48e-01 | 1.60e-03 | 8.36e-01 | 3.83e-01 | 9.90e-01 | 3.83e-01 | 6.64e-01 | 8.05e-01 | 1.62e-01 | 3.93e-01 | 6.51e-06 | 1.23e-39 | 1.25e-01 | 9.78e-01 | 8.27e-03 | 6.78e-01 | 9.10e-01 | 6.78e-01 | 2.88e-02 | 9.78e-01 | 4.80e-01 | 9.79e-01 | 6.71e-01 | 2.35e-01 | 9.78e-01 | 3.83e-01 | 9.78e-01 | 8.36e-01 | 2.23e-05 | 6.40e-11 | 6.53e-02 | 3.26e-01 | 1.77e-02 | 7.91e-03 | 1.08e-46 | 2.79e-02 | 9.78e-01 | 4.57e-01 | 5.73e-01 | 9.48e-01 | 1.60e-03 | 9.78e-01 | 6.66e-01 | 9.76e-01 | 9.91e-01 | 9.78e-01 | 9.97e-01 | 6.59e-01 | 9.78e-01 | 9.89e-01 | 9.78e-01 | 6.15e-01 | 6.60e-01 | 4.79e-03 | 8.28e-01 | 8.41e-01 | 9.78e-01 | 7.38e-01 | 1.17e-01 | 9.78e-01 | 3.88e-01 | 8.61e-01 | 5.99e-01 | 8.39e-01 | 4.58e-02 | 9.23e-01 | 9.96e-01 | 9.37e-01 | 6.78e-01 | 4.51e-02 | 3.30e-02 | 9.78e-01 | 2.07e-01 | 9.78e-01 | 6.23e-01 | 9.78e-01 | 8.42e-01 | 7.61e-10 | 6.97e-02 | 9.78e-01 | 9.78e-01 | 6.46e-01 | 6.67e-05 | 7.71e-05 | 7.81e-01 | 9.78e-01 | 9.78e-01 | 9.78e-01 | 5.53e-01 | 1.96e-03 | 1.12e-08 | 9.78e-01 | 9.59e-01 | 9.78e-01 | 7.97e-01 | 0.00e+00 |
| 10 | 4.61e-01 | 9.88e-01 | 9.89e-01 | 5.82e-01 | 8.48e-01 | 2.98e-01 | 9.88e-01 | 6.97e-01 | 9.88e-01 | 1.27e-01 | 9.66e-01 | 3.04e-22 | 6.02e-06 | 9.88e-01 | 9.23e-01 | 5.21e-01 | 6.52e-01 | 9.34e-01 | 1.82e-01 | 1.43e-03 | 4.88e-10 | 9.96e-01 | 8.75e-01 | 4.88e-01 | 9.96e-01 | 8.96e-01 | 9.66e-01 | 8.26e-01 | 9.88e-01 | 4.80e-01 | 5.35e-01 | 9.70e-01 | 9.79e-01 | 7.70e-02 | 9.93e-01 | 3.20e-01 | 7.61e-01 | 9.59e-01 | 9.22e-01 | 9.88e-01 | 7.57e-07 | 9.88e-01 | 1.14e-09 | 1.10e-01 | 0.00e+00 | 9.88e-01 | 9.79e-01 | 4.57e-01 | 1.86e-01 | 6.96e-11 | 9.88e-01 | 6.81e-01 | 9.79e-01 | 9.89e-01 | 8.48e-01 | 1.57e-02 | 9.82e-01 | 9.79e-01 | 2.34e-01 | 7.98e-14 | 1.25e-01 | 9.79e-01 | 4.34e-01 | 7.52e-31 | 4.16e-01 | 6.71e-01 | 9.00e-01 | 1.74e-01 | 4.74e-01 | 9.92e-01 | 9.71e-01 | 9.79e-01 | 8.20e-01 | 8.35e-01 | 6.85e-01 | 9.88e-01 | 6.19e-02 | 9.81e-01 | 9.88e-01 | 3.27e-02 | 0.00e+00 | 9.28e-01 | 9.98e-01 | 9.87e-01 | 9.98e-01 | 9.24e-02 | 8.00e-01 | 3.31e-70 | 8.87e-01 | 8.01e-01 | 8.59e-01 | 8.75e-01 | 8.57e-01 | 9.98e-01 | 6.43e-60 | 9.79e-01 | 9.49e-02 | 2.58e-02 | 1.19e-43 | 9.88e-01 | 6.22e-01 | 4.31e-02 | 9.88e-01 | 8.75e-01 | 7.64e-01 | 8.48e-01 | 9.79e-01 | 9.86e-01 | 9.67e-01 | 3.07e-01 | 9.10e-01 | 9.45e-01 | 9.88e-01 | 9.48e-01 | 9.79e-01 | 7.21e-05 | 9.88e-01 | 5.82e-01 | 1.31e-01 | 9.88e-01 | 7.12e-01 | 9.88e-01 | 8.96e-01 | 9.14e-01 | 8.48e-01 | 9.88e-01 | 9.88e-01 | 7.10e-01 | 1.55e-01 | 9.88e-01 | 8.81e-01 | 8.48e-01 | 9.72e-01 | 8.48e-01 | 6.94e-01 | 7.80e-01 | 6.05e-03 | 5.54e-01 | 6.57e-01 | 6.83e-38 | 8.95e-01 | 9.88e-01 | 8.02e-01 | 8.48e-01 | 9.19e-01 | 8.48e-01 | 1.73e-02 | 9.88e-01 | 9.01e-01 | 9.91e-01 | 9.64e-01 | 5.51e-01 | 9.88e-01 | 8.48e-01 | 9.79e-01 | 9.34e-01 | 4.27e-01 | 7.13e-14 | 4.18e-02 | 7.34e-01 | 3.56e-02 | 6.66e-03 | 8.23e-60 | 8.83e-01 | 9.88e-01 | 8.14e-01 | 4.49e-01 | 9.95e-01 | 9.88e-01 | 9.79e-01 | 7.59e-01 | 1.97e-73 | 5.04e-01 | 9.79e-01 | 9.72e-01 | 3.40e-01 | 9.88e-01 | 9.89e-01 | 9.88e-01 | 8.11e-01 | 9.90e-01 | 2.47e-03 | 8.75e-01 | 8.86e-01 | 9.88e-01 | 3.06e-01 | 7.08e-01 | 9.79e-01 | 6.53e-01 | 9.99e-01 | 9.77e-01 | 8.84e-01 | 6.83e-01 | 9.36e-01 | 9.96e-01 | 9.88e-01 | 5.29e-05 | 1.94e-38 | 9.88e-01 | 9.88e-01 | 3.41e-02 | 9.88e-01 | 9.53e-01 | 8.48e-01 | 8.87e-01 | 8.81e-01 | 5.98e-01 | 9.79e-01 | 9.88e-01 | 9.30e-01 | 2.95e-01 | 1.25e-09 | 9.96e-01 | 9.88e-01 | 9.79e-01 | 9.88e-01 | 9.39e-01 | 1.22e-01 | 4.54e-01 | 9.79e-01 | 9.80e-01 | 9.79e-01 | 9.41e-01 | 9.79e-01 |
| 11 | 8.05e-01 | 9.99e-01 | 9.97e-01 | 7.94e-01 | 6.73e-01 | 5.36e-01 | 9.99e-01 | 9.62e-01 | 9.99e-01 | 6.97e-01 | 9.61e-01 | 7.48e-16 | 4.11e-07 | 9.99e-01 | 9.99e-01 | 7.70e-01 | 9.52e-01 | 9.36e-01 | 2.99e-02 | 7.02e-01 | 5.00e-06 | 9.92e-01 | 9.90e-01 | 9.72e-01 | 9.95e-01 | 9.87e-01 | 9.99e-01 | 6.30e-01 | 9.99e-01 | 9.05e-01 | 8.13e-01 | 9.76e-01 | 9.99e-01 | 6.70e-01 | 9.90e-01 | 7.18e-01 | 9.99e-01 | 9.01e-01 | 8.23e-01 | 9.99e-01 | 6.66e-03 | 9.99e-01 | 2.29e-92 | 2.10e-01 | 9.99e-01 | 9.99e-01 | 9.99e-01 | 1.54e-21 | 3.20e-28 | 3.08e-08 | 9.99e-01 | 9.58e-01 | 9.99e-01 | 9.83e-01 | 6.73e-01 | 3.42e-01 | 9.52e-01 | 9.70e-01 | 7.68e-01 | 3.08e-13 | 1.22e-02 | 9.99e-01 | 8.84e-01 | 8.75e-30 | 3.64e-01 | 6.72e-01 | 9.76e-01 | 4.95e-01 | 1.80e-03 | 9.89e-01 | 9.95e-01 | 9.99e-01 | 9.99e-01 | 9.88e-01 | 6.48e-01 | 9.99e-01 | 9.92e-01 | 9.71e-01 | 9.99e-01 | 1.67e-01 | 0.00e+00 | 9.09e-01 | 1.00e+00 | 9.96e-01 | 9.90e-01 | 4.95e-04 | 7.23e-03 | 1.23e-68 | 9.91e-01 | 7.47e-01 | 6.56e-01 | 9.90e-01 | 9.37e-01 | 9.90e-01 | 7.74e-59 | 9.99e-01 | 4.73e-01 | 1.17e-02 | 9.98e-01 | 9.99e-01 | 9.63e-01 | 1.88e-01 | 9.99e-01 | 9.90e-01 | 8.65e-01 | 8.77e-01 | 9.99e-01 | 9.82e-01 | 9.85e-01 | 8.20e-01 | 1.26e-10 | 7.13e-01 | 9.99e-01 | 9.76e-01 | 9.99e-01 | 1.35e-09 | 9.55e-01 | 5.58e-01 | 6.54e-12 | 9.99e-01 | 7.28e-01 | 2.82e-09 | 9.92e-01 | 7.93e-01 | 9.70e-01 | 2.82e-09 | 9.99e-01 | 9.47e-01 | 8.04e-01 | 2.82e-09 | 9.90e-01 | 6.73e-01 | 9.77e-01 | 6.73e-01 | 6.57e-01 | 4.74e-01 | 1.14e-01 | 8.38e-01 | 9.53e-01 | 1.21e-37 | 9.75e-01 | 9.99e-01 | 1.94e-02 | 6.73e-01 | 9.92e-01 | 8.77e-01 | 3.53e-01 | 9.99e-01 | 7.85e-01 | 9.71e-01 | 8.59e-01 | 6.10e-01 | 9.99e-01 | 6.73e-01 | 9.99e-01 | 9.62e-01 | 1.74e-06 | 4.45e-15 | 3.80e-01 | 8.36e-01 | 2.60e-03 | 9.30e-07 | 2.88e-59 | 9.98e-01 | 9.99e-01 | 6.55e-01 | 8.42e-01 | 9.79e-01 | 9.99e-01 | 9.70e-01 | 9.72e-01 | 9.99e-01 | 5.40e-01 | 9.70e-01 | 9.50e-01 | 2.20e-01 | 9.99e-01 | 9.97e-01 | 9.99e-01 | 9.96e-01 | 9.53e-01 | 9.02e-02 | 9.90e-01 | 9.91e-01 | 9.99e-01 | 9.21e-01 | 6.08e-01 | 9.70e-01 | 9.52e-01 | 6.67e-01 | 9.55e-01 | 9.91e-01 | 2.32e-01 | 8.46e-01 | 9.92e-01 | 9.57e-01 | 8.77e-01 | 2.92e-37 | 9.92e-01 | 9.99e-01 | 3.99e-01 | 9.99e-01 | 5.17e-01 | 8.77e-01 | 9.91e-01 | 9.90e-01 | 6.21e-01 | 9.99e-01 | 9.99e-01 | 9.22e-01 | 2.29e-05 | 8.44e-21 | 9.33e-01 | 9.99e-01 | 9.70e-01 | 9.99e-01 | 8.31e-01 | 4.59e-02 | 2.66e-02 | 9.99e-01 | 9.97e-01 | 9.99e-01 | 9.99e-01 | 0.00e+00 |
| 12 | 9.67e-01 | 9.98e-01 | 9.99e-01 | 9.64e-01 | 8.63e-01 | 5.20e-01 | 9.98e-01 | 8.49e-01 | 8.63e-01 | 7.74e-01 | 9.89e-01 | 1.49e-15 | 2.63e-04 | 9.98e-01 | 9.99e-01 | 4.81e-01 | 8.17e-01 | 7.41e-01 | 6.00e-03 | 1.27e-01 | 5.49e-10 | 9.64e-01 | 9.55e-01 | 3.39e-02 | 9.99e-01 | 9.99e-01 | 9.93e-01 | 9.30e-01 | 9.98e-01 | 9.14e-01 | 8.65e-01 | 9.77e-01 | 9.98e-01 | 5.40e-01 | 9.98e-01 | 5.28e-01 | 9.75e-01 | 9.88e-01 | 9.33e-01 | 8.63e-01 | 1.78e-04 | 9.98e-01 | 1.08e-08 | 4.62e-01 | 0.00e+00 | 9.98e-01 | 9.97e-01 | 3.37e-21 | 8.24e-28 | 1.17e-09 | 9.98e-01 | 8.38e-01 | 9.97e-01 | 9.98e-01 | 8.63e-01 | 4.33e-02 | 7.69e-01 | 9.97e-01 | 8.40e-01 | 1.62e-12 | 1.35e-02 | 9.98e-01 | 9.19e-01 | 7.40e-29 | 6.37e-01 | 7.90e-01 | 8.95e-01 | 3.67e-02 | 3.55e-03 | 9.99e-01 | 9.79e-01 | 9.98e-01 | 9.94e-01 | 5.83e-01 | 8.49e-01 | 8.63e-01 | 9.73e-01 | 9.63e-01 | 9.98e-01 | 1.86e-01 | 0.00e+00 | 8.87e-01 | 9.91e-01 | 8.72e-01 | 9.55e-01 | 9.74e-03 | 6.32e-03 | 2.65e-67 | 9.60e-01 | 4.41e-01 | 8.28e-01 | 9.55e-01 | 9.38e-01 | 1.00e+00 | 1.87e-57 | 9.98e-01 | 6.16e-01 | 2.92e-01 | 5.96e-42 | 8.63e-01 | 5.93e-01 | 1.30e-01 | 8.63e-01 | 9.90e-01 | 8.65e-01 | 8.63e-01 | 9.98e-01 | 9.14e-01 | 9.19e-01 | 8.04e-01 | 2.29e-10 | 8.55e-01 | 8.63e-01 | 9.65e-01 | 9.98e-01 | 4.60e-03 | 9.98e-01 | 2.24e-01 | 2.05e-11 | 9.98e-01 | 2.02e-04 | 8.49e-09 | 3.58e-01 | 7.43e-01 | 9.97e-01 | 8.49e-09 | 9.98e-01 | 9.98e-01 | 8.31e-01 | 8.49e-09 | 9.58e-01 | 8.63e-01 | 9.99e-01 | 9.97e-01 | 8.98e-01 | 7.44e-01 | 1.31e-01 | 5.27e-01 | 6.68e-01 | 9.53e-40 | 6.16e-01 | 9.98e-01 | 2.04e-02 | 9.97e-01 | 9.95e-01 | 8.63e-01 | 3.86e-01 | 9.98e-01 | 2.28e-02 | 1.00e+00 | 9.21e-01 | 5.30e-01 | 9.98e-01 | 8.63e-01 | 9.97e-01 | 9.49e-01 | 1.00e-04 | 3.54e-16 | 5.46e-01 | 6.43e-01 | 2.06e-03 | 1.63e-05 | 8.93e-58 | 2.38e-01 | 8.63e-01 | 4.50e-01 | 7.78e-01 | 9.78e-01 | 8.63e-01 | 9.97e-01 | 9.76e-01 | 2.88e-70 | 4.61e-01 | 9.97e-01 | 1.00e+00 | 7.34e-02 | 9.98e-01 | 9.99e-01 | 8.63e-01 | 4.02e-02 | 4.78e-01 | 1.77e-01 | 9.55e-01 | 9.60e-01 | 8.63e-01 | 9.78e-01 | 4.58e-01 | 9.97e-01 | 4.36e-02 | 7.63e-01 | 8.26e-01 | 9.59e-01 | 3.10e-01 | 5.43e-01 | 9.65e-01 | 9.61e-01 | 8.63e-01 | 4.71e-37 | 9.98e-01 | 9.98e-01 | 2.25e-01 | 8.63e-01 | 6.72e-01 | 8.63e-01 | 9.60e-01 | 3.15e-01 | 7.40e-01 | 9.98e-01 | 8.63e-01 | 9.81e-01 | 2.32e-04 | 3.65e-09 | 9.72e-01 | 9.98e-01 | 9.97e-01 | 9.98e-01 | 9.37e-01 | 7.77e-03 | 2.96e-02 | 9.97e-01 | 9.93e-01 | 9.98e-01 | 9.99e-01 | 0.00e+00 |
| 13 | 5.18e-01 | 1.00e+00 | 9.41e-01 | 9.25e-01 | 8.75e-01 | 4.75e-01 | 1.00e+00 | 9.60e-01 | 1.00e+00 | 5.58e-01 | 9.97e-01 | 2.68e-18 | 3.02e-10 | 1.00e+00 | 9.98e-01 | 9.46e-01 | 9.48e-01 | 9.21e-01 | 3.21e-01 | 1.50e-01 | 4.77e-06 | 9.93e-01 | 9.91e-01 | 9.37e-01 | 9.90e-01 | 9.74e-01 | 9.96e-01 | 5.17e-01 | 1.00e+00 | 3.21e-01 | 9.48e-01 | 9.78e-01 | 9.10e-01 | 7.43e-01 | 9.81e-01 | 9.39e-01 | 9.98e-01 | 9.93e-01 | 9.45e-01 | 1.00e+00 | 1.25e-06 | 1.00e+00 | 2.97e-89 | 3.24e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 2.59e-20 | 7.26e-27 | 1.92e-09 | 1.00e+00 | 9.56e-01 | 1.00e+00 | 9.38e-01 | 8.75e-01 | 2.19e-01 | 9.49e-01 | 9.53e-01 | 7.11e-01 | 2.27e-12 | 1.49e-02 | 9.10e-01 | 8.62e-01 | 1.52e-28 | 9.66e-01 | 8.03e-01 | 8.49e-01 | 1.08e-01 | 4.57e-03 | 9.78e-01 | 9.90e-01 | 1.00e+00 | 9.99e-01 | 9.31e-01 | 7.43e-01 | 1.00e+00 | 9.92e-01 | 9.19e-01 | 1.00e+00 | 2.03e-01 | 0.00e+00 | 9.39e-01 | 7.76e-01 | 9.81e-01 | 9.91e-01 | 2.49e-06 | 1.48e-02 | 2.46e-66 | 9.92e-01 | 7.06e-01 | 8.64e-01 | 9.91e-01 | 8.04e-01 | 9.98e-01 | 5.68e-57 | 1.00e+00 | 3.34e-01 | 6.04e-02 | 5.29e-41 | 1.00e+00 | 6.01e-01 | 1.24e-01 | 1.00e+00 | 9.91e-01 | 9.38e-01 | 8.75e-01 | 1.00e+00 | 9.91e-01 | 9.90e-01 | 7.78e-01 | 6.33e-10 | 9.42e-01 | 1.00e+00 | 8.41e-01 | 9.10e-01 | 1.19e-09 | 9.96e-01 | 5.22e-01 | 3.29e-11 | 1.00e+00 | 8.36e-01 | 2.42e-08 | 9.93e-01 | 9.97e-01 | 9.10e-01 | 2.42e-08 | 1.00e+00 | 9.66e-01 | 7.02e-01 | 2.42e-08 | 9.92e-01 | 8.75e-01 | 9.99e-01 | 8.75e-01 | 9.50e-01 | 7.12e-01 | 8.32e-02 | 4.33e-01 | 9.50e-01 | 1.15e-35 | 9.86e-01 | 1.00e+00 | 6.87e-02 | 8.75e-01 | 9.92e-01 | 8.75e-01 | 2.30e-01 | 1.00e+00 | 9.51e-01 | 9.84e-01 | 9.50e-01 | 6.15e-01 | 1.00e+00 | 8.75e-01 | 1.00e+00 | 9.73e-01 | 2.12e-04 | 2.68e-12 | 2.29e-01 | 4.71e-01 | 5.51e-03 | 1.56e-05 | 1.73e-57 | 9.89e-01 | 1.00e+00 | 7.11e-01 | 8.56e-01 | 9.86e-01 | 1.00e+00 | 9.53e-01 | 9.62e-01 | 1.36e-69 | 5.56e-01 | 9.53e-01 | 9.89e-01 | 2.54e-01 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 9.96e-01 | 9.24e-01 | 6.23e-02 | 9.91e-01 | 9.92e-01 | 1.00e+00 | 8.38e-01 | 8.60e-01 | 9.10e-01 | 9.48e-01 | 7.14e-01 | 9.52e-01 | 9.92e-01 | 2.91e-01 | 9.84e-01 | 9.94e-01 | 8.79e-01 | 9.10e-01 | 1.08e-35 | 9.79e-01 | 1.00e+00 | 2.40e-01 | 1.00e+00 | 5.79e-01 | 8.75e-01 | 9.92e-01 | 9.92e-01 | 5.88e-01 | 1.00e+00 | 1.00e+00 | 9.80e-01 | 8.66e-05 | 3.37e-19 | 9.35e-01 | 1.00e+00 | 9.10e-01 | 1.00e+00 | 9.33e-01 | 8.30e-02 | 4.96e-02 | 9.53e-01 | 9.51e-01 | 9.10e-01 | 1.00e+00 | 0.00e+00 |
| 14 | 8.49e-01 | 1.00e+00 | 9.98e-01 | 5.82e-01 | 9.86e-01 | 6.45e-01 | 1.00e+00 | 9.21e-01 | 1.00e+00 | 3.22e-01 | 9.95e-01 | 2.99e-12 | 2.14e-10 | 1.00e+00 | 9.58e-01 | 9.28e-01 | 9.75e-01 | 3.44e-01 | 2.48e-01 | 6.16e-01 | 2.09e-09 | 9.87e-01 | 9.82e-01 | 8.65e-01 | 9.99e-01 | 9.98e-01 | 9.98e-01 | 8.54e-01 | 6.32e-01 | 6.31e-01 | 8.09e-01 | 9.91e-01 | 9.95e-01 | 6.78e-01 | 9.98e-01 | 6.08e-01 | 8.97e-01 | 9.97e-01 | 9.96e-01 | 1.00e+00 | 1.02e-06 | 1.00e+00 | 1.58e-89 | 2.32e-01 | 0.00e+00 | 1.00e+00 | 9.95e-01 | 7.20e-20 | 1.02e-26 | 2.49e-08 | 1.00e+00 | 9.14e-01 | 9.86e-01 | 9.92e-01 | 1.00e+00 | 7.17e-02 | 9.00e-01 | 9.95e-01 | 8.39e-01 | 5.37e-12 | 3.22e-02 | 9.95e-01 | 7.49e-01 | 2.12e-27 | 9.88e-01 | 9.26e-01 | 8.54e-01 | 8.16e-01 | 5.52e-03 | 9.98e-01 | 9.88e-01 | 1.00e+00 | 9.99e-01 | 7.46e-01 | 9.58e-01 | 1.00e+00 | 9.91e-01 | 9.07e-01 | 6.32e-01 | 1.49e-02 | 0.00e+00 | 9.95e-01 | 9.67e-01 | 9.97e-01 | 9.83e-01 | 3.50e-04 | 3.31e-02 | 6.41e-65 | 9.85e-01 | 5.09e-01 | 6.94e-01 | 9.82e-01 | 9.20e-01 | 1.00e+00 | 1.78e-55 | 1.00e+00 | 3.10e-01 | 1.43e-01 | 2.18e-40 | 1.00e+00 | 9.78e-01 | 9.25e-02 | 1.00e+00 | 9.82e-01 | 8.81e-01 | 9.95e-01 | 1.00e+00 | 7.81e-01 | 9.94e-01 | 6.21e-01 | 1.53e-09 | 6.66e-01 | 1.00e+00 | 9.89e-01 | 6.32e-01 | 5.75e-10 | 9.99e-01 | 3.50e-01 | 9.67e-11 | 1.00e+00 | 2.24e-14 | 6.54e-08 | 9.87e-01 | 9.98e-01 | 9.86e-01 | 6.54e-08 | 6.32e-01 | 9.94e-01 | 5.27e-01 | 6.54e-08 | 9.20e-01 | 9.86e-01 | 1.00e+00 | 9.86e-01 | 8.21e-01 | 9.06e-01 | 1.25e-01 | 3.98e-01 | 9.02e-01 | 2.99e-74 | 7.89e-01 | 1.00e+00 | 1.58e-01 | 9.86e-01 | 9.86e-01 | 9.43e-01 | 3.30e-01 | 1.00e+00 | 9.95e-01 | 9.98e-01 | 9.99e-01 | 6.82e-01 | 1.00e+00 | 9.86e-01 | 9.95e-01 | 9.84e-01 | 6.88e-04 | 1.34e-16 | 3.46e-01 | 8.25e-01 | 2.48e-02 | 1.48e-03 | 8.68e-56 | 9.87e-01 | 1.00e+00 | 7.84e-01 | 8.91e-01 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 9.28e-01 | 6.19e-68 | 6.38e-01 | 9.95e-01 | 9.99e-01 | 8.17e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 9.07e-01 | 9.07e-01 | 2.14e-01 | 9.82e-01 | 4.91e-02 | 1.00e+00 | 8.14e-01 | 7.60e-02 | 9.86e-01 | 9.00e-01 | 9.36e-01 | 9.36e-01 | 9.84e-01 | 1.91e-01 | 8.50e-01 | 9.99e-01 | 9.91e-01 | 9.43e-01 | 5.59e-35 | 9.97e-01 | 1.00e+00 | 7.33e-02 | 1.00e+00 | 6.82e-01 | 9.95e-01 | 9.85e-01 | 9.84e-01 | 7.89e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.67e-04 | 2.09e-18 | 9.94e-01 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 9.96e-01 | 3.20e-02 | 5.24e-02 | 9.95e-01 | 9.95e-01 | 9.95e-01 | 1.00e+00 | 0.00e+00 |
| 15 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 9.53e-01 | 1.00e+00 | 2.18e-01 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 6.06e-01 | 1.00e+00 | 3.84e-15 | 3.21e-12 | 1.00e+00 | 9.99e-01 | 9.94e-01 | 9.66e-01 | 9.99e-01 | 4.56e-01 | 2.75e-05 | 2.30e-10 | 9.97e-01 | 9.96e-01 | 5.84e-01 | 1.00e+00 | 4.67e-01 | 1.00e+00 | 9.48e-01 | 1.00e+00 | 9.78e-01 | 9.71e-01 | 1.00e+00 | 1.00e+00 | 8.57e-01 | 1.00e+00 | 9.70e-01 | 9.57e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 4.10e-06 | 1.00e+00 | 3.47e-86 | 1.14e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 4.35e-19 | 3.17e-25 | 4.14e-06 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 2.18e-01 | 9.66e-01 | 1.00e+00 | 7.47e-01 | 3.24e-11 | 3.37e-02 | 1.00e+00 | 8.93e-01 | 1.43e-26 | 8.21e-01 | 7.04e-01 | 9.90e-01 | 1.07e-01 | 1.14e-02 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.72e-01 | 9.69e-01 | 1.00e+00 | 9.99e-01 | 7.53e-06 | 1.00e+00 | 4.11e-02 | 0.00e+00 | 9.91e-01 | 1.00e+00 | 9.99e-01 | 9.96e-01 | 1.88e-02 | 1.02e-01 | 2.32e-63 | 9.96e-01 | 3.62e-01 | 8.01e-01 | 9.96e-01 | 9.71e-01 | 1.00e+00 | 7.39e-54 | 1.00e+00 | 2.81e-01 | 1.97e-01 | 1.13e-38 | 1.00e+00 | 9.39e-01 | 4.11e-01 | 1.00e+00 | 9.96e-01 | 9.88e-01 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 8.14e-01 | 9.80e-09 | 8.09e-05 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 7.08e-08 | 1.00e+00 | 5.04e-01 | 2.02e-10 | 1.00e+00 | 1.09e-01 | 1.69e-07 | 9.97e-01 | 9.89e-01 | 1.00e+00 | 1.69e-07 | 1.00e+00 | 1.00e+00 | 8.03e-01 | 1.69e-07 | 9.96e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.47e-01 | 9.35e-01 | 4.34e-01 | 8.38e-01 | 9.67e-01 | 2.58e-35 | 9.97e-01 | 1.00e+00 | 2.68e-01 | 0.00e+00 | 9.98e-01 | 9.90e-01 | 2.30e-01 | 1.00e+00 | 4.96e-01 | 1.00e+00 | 9.95e-01 | 9.12e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 7.23e-05 | 1.81e-20 | 1.91e-06 | 8.35e-01 | 1.13e-02 | 4.77e-04 | 3.02e-54 | 9.88e-03 | 1.00e+00 | 9.67e-01 | 9.80e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 3.06e-66 | 8.87e-01 | 1.00e+00 | 1.00e+00 | 1.72e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 1.00e+00 | 8.10e-03 | 9.96e-01 | 9.96e-01 | 1.00e+00 | 9.78e-01 | 2.69e-05 | 1.00e+00 | 9.66e-01 | 9.25e-01 | 9.69e-01 | 9.96e-01 | 4.05e-01 | 9.99e-01 | 1.00e+00 | 9.66e-01 | 9.90e-01 | 4.32e-34 | 1.00e+00 | 1.00e+00 | 2.37e-01 | 1.00e+00 | 7.54e-01 | 9.90e-01 | 9.96e-01 | 9.96e-01 | 3.67e-09 | 1.00e+00 | 1.00e+00 | 9.71e-01 | 1.04e-03 | 1.22e-18 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 4.40e-01 | 9.26e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 |
| 16 | 1.31e-24 | 1.00e+00 | 1.00e+00 | 9.51e-01 | 4.64e-01 | 5.29e-01 | 1.00e+00 | 9.76e-01 | 1.00e+00 | 5.80e-01 | 1.00e+00 | 3.42e-15 | 4.81e-12 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 9.67e-01 | 9.99e-01 | 3.36e-01 | 9.55e-03 | 1.32e-09 | 9.97e-01 | 9.96e-01 | 1.10e-05 | 1.00e+00 | 2.07e-06 | 1.00e+00 | 8.32e-01 | 1.00e+00 | 9.71e-01 | 9.72e-01 | 1.00e+00 | 1.00e+00 | 8.59e-01 | 1.00e+00 | 9.67e-01 | 9.11e-01 | 1.00e+00 | 6.51e-01 | 1.00e+00 | 2.20e-06 | 1.00e+00 | 1.07e-84 | 7.08e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.57e-18 | 7.31e-25 | 5.59e-10 | 1.00e+00 | 9.73e-01 | 4.64e-01 | 1.00e+00 | 9.96e-01 | 1.85e-01 | 9.68e-01 | 1.00e+00 | 7.35e-01 | 3.07e-12 | 3.88e-02 | 1.00e+00 | 8.91e-01 | 6.37e-26 | 8.97e-01 | 4.06e-06 | 9.86e-01 | 2.83e-01 | 1.64e-02 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.80e-01 | 9.87e-01 | 1.00e+00 | 1.00e+00 | 9.82e-01 | 1.00e+00 | 1.58e-05 | 0.00e+00 | 9.90e-01 | 1.00e+00 | 9.99e-01 | 9.96e-01 | 1.14e-02 | 4.24e-02 | 3.65e-62 | 9.97e-01 | 6.64e-01 | 7.80e-01 | 9.96e-01 | 2.04e-17 | 1.00e+00 | 9.40e-53 | 1.00e+00 | 8.70e-04 | 1.41e-01 | 1.04e-37 | 1.00e+00 | 9.73e-01 | 3.90e-01 | 1.00e+00 | 9.96e-01 | 9.88e-01 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.07e-01 | 2.91e-08 | 9.85e-01 | 1.00e+00 | 9.85e-01 | 1.00e+00 | 9.02e-09 | 1.00e+00 | 4.25e-01 | 4.17e-10 | 1.00e+00 | 8.88e-14 | 4.17e-07 | 9.97e-01 | 4.67e-01 | 1.00e+00 | 4.17e-07 | 1.00e+00 | 1.00e+00 | 8.06e-01 | 4.17e-07 | 9.97e-01 | 4.64e-01 | 1.00e+00 | 1.00e+00 | 9.72e-01 | 9.46e-01 | 3.26e-01 | 9.10e-01 | 9.68e-01 | 2.48e-71 | 9.98e-01 | 1.00e+00 | 3.17e-01 | 9.96e-01 | 9.99e-01 | 9.96e-01 | 1.96e-01 | 1.00e+00 | 5.73e-01 | 1.00e+00 | 7.74e-01 | 9.59e-01 | 1.00e+00 | 4.64e-01 | 1.00e+00 | 9.99e-01 | 2.18e-04 | 5.79e-22 | 3.11e-01 | 7.98e-01 | 2.47e-04 | 3.50e-03 | 2.16e-54 | 9.99e-01 | 1.00e+00 | 9.86e-01 | 9.92e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.87e-01 | 5.54e-65 | 9.46e-01 | 1.00e+00 | 1.00e+00 | 2.56e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 6.94e-06 | 9.96e-01 | 9.97e-01 | 1.00e+00 | 9.76e-01 | 8.44e-01 | 1.00e+00 | 9.68e-01 | 9.45e-01 | 9.70e-01 | 9.97e-01 | 4.10e-01 | 9.99e-01 | 1.00e+00 | 1.68e-01 | 9.96e-01 | 3.00e-33 | 1.00e+00 | 1.00e+00 | 1.99e-01 | 1.00e+00 | 8.06e-01 | 9.96e-01 | 9.97e-01 | 9.97e-01 | 8.68e-01 | 1.00e+00 | 1.00e+00 | 9.77e-01 | 1.42e-03 | 1.56e-18 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.93e-01 | 3.71e-01 | 1.16e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 |
| 17 | 9.96e-01 | 9.99e-01 | 9.98e-01 | 9.53e-01 | 9.88e-01 | 2.36e-01 | 9.96e-01 | 9.91e-01 | 9.99e-01 | 8.66e-01 | 1.00e+00 | 1.00e-14 | 4.17e-10 | 9.96e-01 | 1.00e+00 | 8.53e-01 | 9.61e-01 | 9.97e-01 | 2.07e-01 | 3.39e-04 | 3.11e-11 | 9.99e-01 | 1.00e+00 | 8.39e-01 | 1.00e+00 | 9.94e-01 | 9.99e-01 | 9.10e-09 | 9.99e-01 | 7.79e-01 | 9.73e-01 | 9.97e-01 | 9.96e-01 | 3.94e-01 | 9.95e-01 | 8.83e-01 | 8.69e-06 | 9.91e-01 | 8.25e-01 | 9.99e-01 | 8.76e-10 | 9.99e-01 | 1.65e-83 | 2.72e-01 | 0.00e+00 | 9.96e-01 | 9.88e-01 | 1.19e-03 | 3.42e-16 | 2.48e-07 | 9.99e-01 | 7.84e-01 | 0.00e+00 | 9.99e-01 | 9.99e-01 | 4.97e-01 | 9.11e-03 | 9.88e-01 | 8.90e-01 | 3.92e-10 | 1.15e-04 | 9.96e-01 | 9.49e-01 | 3.90e-25 | 9.51e-01 | 7.63e-01 | 1.65e-05 | 2.43e-02 | 1.91e-02 | 1.00e+00 | 9.98e-01 | 9.96e-01 | 9.97e-01 | 6.82e-01 | 8.42e-01 | 9.99e-01 | 9.73e-01 | 9.87e-01 | 9.99e-01 | 1.72e-02 | 0.00e+00 | 9.67e-01 | 8.67e-01 | 9.99e-01 | 8.43e-01 | 3.65e-03 | 6.03e-01 | 3.61e-61 | 9.99e-01 | 2.42e-01 | 3.03e-08 | 1.00e+00 | 6.44e-01 | 1.00e+00 | 5.45e-52 | 9.96e-01 | 1.27e-02 | 9.02e-02 | 2.90e-37 | 9.99e-01 | 9.97e-01 | 7.05e-01 | 9.99e-01 | 9.57e-01 | 7.83e-01 | 9.88e-01 | 9.96e-01 | 9.92e-01 | 7.93e-01 | 7.93e-01 | 3.56e-08 | 9.97e-01 | 9.99e-01 | 9.51e-01 | 9.96e-01 | 6.51e-08 | 9.98e-01 | 7.22e-01 | 7.91e-06 | 9.96e-01 | 6.75e-05 | 9.48e-02 | 9.54e-01 | 8.20e-01 | 9.99e-01 | 9.48e-02 | 9.99e-01 | 1.00e+00 | 9.40e-01 | 9.48e-02 | 9.60e-01 | 9.88e-01 | 9.98e-01 | 9.88e-01 | 9.92e-01 | 9.96e-01 | 3.68e-01 | 1.22e-04 | 1.94e-03 | 9.10e-37 | 7.66e-01 | 9.96e-01 | 2.43e-02 | 9.99e-01 | 9.95e-01 | 9.88e-01 | 2.60e-01 | 9.99e-01 | 1.52e-02 | 9.88e-01 | 9.70e-01 | 7.98e-01 | 9.99e-01 | 9.88e-01 | 9.99e-01 | 9.95e-01 | 2.89e-03 | 3.71e-25 | 4.29e-01 | 8.59e-01 | 2.76e-03 | 1.96e-04 | 8.70e-53 | 2.93e-01 | 9.99e-01 | 4.87e-01 | 9.41e-01 | 1.00e+00 | 9.48e-02 | 9.99e-01 | 8.30e-01 | 4.65e-64 | 8.26e-01 | 9.99e-01 | 1.00e+00 | 7.45e-04 | 9.96e-01 | 9.93e-01 | 9.99e-01 | 7.52e-01 | 9.95e-01 | 7.69e-04 | 1.00e+00 | 9.63e-01 | 9.99e-01 | 9.98e-01 | 3.75e-01 | 9.99e-01 | 8.22e-01 | 9.63e-01 | 9.92e-01 | 1.00e+00 | 1.83e-01 | 9.87e-01 | 9.99e-01 | 1.00e+00 | 9.88e-01 | 9.72e-33 | 1.00e+00 | 9.99e-01 | 7.13e-01 | 9.99e-01 | 9.92e-01 | 9.88e-01 | 9.99e-01 | 2.36e-06 | 3.24e-01 | 9.96e-01 | 9.99e-01 | 9.99e-01 | 1.41e-02 | 5.54e-37 | 2.98e-24 | 9.96e-01 | 9.99e-01 | 9.99e-01 | 9.93e-01 | 3.68e-01 | 1.34e-05 | 9.99e-01 | 9.98e-01 | 9.96e-01 | 1.00e+00 | 0.00e+00 |
| 18 | 9.09e-01 | 1.00e+00 | 1.00e+00 | 7.37e-01 | 9.99e-01 | 4.12e-01 | 1.00e+00 | 8.90e-01 | 1.00e+00 | 1.09e-01 | 9.90e-01 | 4.24e-18 | 3.23e-10 | 9.99e-01 | 1.00e+00 | 9.28e-01 | 9.94e-01 | 9.82e-01 | 1.17e-01 | 7.60e-07 | 1.17e-11 | 1.00e+00 | 9.81e-01 | 9.22e-01 | 9.99e-01 | 9.98e-01 | 9.99e-01 | 5.31e-08 | 1.00e+00 | 4.54e-01 | 9.95e-01 | 7.27e-01 | 9.99e-01 | 5.21e-01 | 9.99e-01 | 5.40e-01 | 1.59e-05 | 9.98e-01 | 9.98e-01 | 1.00e+00 | 2.87e-05 | 1.00e+00 | 5.82e-82 | 8.10e-03 | 0.00e+00 | 9.99e-01 | 9.99e-01 | 7.49e-18 | 2.84e-24 | 4.45e-10 | 1.00e+00 | 8.78e-01 | 0.00e+00 | 9.98e-01 | 9.94e-01 | 1.41e-01 | 9.99e-01 | 9.96e-01 | 7.19e-01 | 8.22e-11 | 2.01e-02 | 9.99e-01 | 6.19e-01 | 3.14e-24 | 7.34e-01 | 9.23e-01 | 9.88e-01 | 7.70e-01 | 1.81e-02 | 9.97e-01 | 9.99e-01 | 9.99e-01 | 9.96e-01 | 9.99e-01 | 7.13e-01 | 1.00e+00 | 8.66e-01 | 9.98e-01 | 9.94e-01 | 4.64e-02 | 0.00e+00 | 9.96e-01 | 9.42e-01 | 9.74e-01 | 1.00e+00 | 9.00e-04 | 1.22e-02 | 3.51e-60 | 9.84e-01 | 9.79e-01 | 7.72e-01 | 1.00e+00 | 9.05e-01 | 1.00e+00 | 3.25e-51 | 9.99e-01 | 7.22e-01 | 5.52e-01 | 5.06e-37 | 1.00e+00 | 9.31e-01 | 6.96e-01 | 1.00e+00 | 9.81e-01 | 7.92e-01 | 9.94e-01 | 9.99e-01 | 9.99e-01 | 9.99e-01 | 9.89e-01 | 1.61e-07 | 2.62e-01 | 1.00e+00 | 9.42e-01 | 9.99e-01 | 1.80e-07 | 9.97e-01 | 2.64e-01 | 5.64e-09 | 9.99e-01 | 8.81e-12 | 2.25e-06 | 9.87e-01 | 1.00e+00 | 9.96e-01 | 2.25e-06 | 9.94e-01 | 9.99e-01 | 6.94e-01 | 2.25e-06 | 9.99e-01 | 9.96e-01 | 9.98e-01 | 9.96e-01 | 9.85e-01 | 9.92e-01 | 9.59e-02 | 9.13e-01 | 8.59e-01 | 3.81e-69 | 1.00e+00 | 1.00e+00 | 1.74e-01 | 0.00e+00 | 1.00e+00 | 9.94e-01 | 1.55e-01 | 1.00e+00 | 9.97e-01 | 9.97e-01 | 9.99e-01 | 9.62e-01 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 9.98e-01 | 1.10e-04 | 7.98e-14 | 4.29e-01 | 4.53e-01 | 6.56e-09 | 2.86e-08 | 3.87e-51 | 9.64e-01 | 1.00e+00 | 2.29e-01 | 9.90e-01 | 9.99e-01 | 1.00e+00 | 9.94e-01 | 9.07e-01 | 4.68e-63 | 8.78e-01 | 9.94e-01 | 1.00e+00 | 8.82e-02 | 9.99e-01 | 9.98e-01 | 1.00e+00 | 9.53e-01 | 9.98e-01 | 2.30e-02 | 1.00e+00 | 9.84e-01 | 1.00e+00 | 8.17e-01 | 2.77e-16 | 9.96e-01 | 8.55e-01 | 6.68e-01 | 1.00e+00 | 9.84e-01 | 3.07e-09 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 3.81e-01 | 3.30e-33 | 9.97e-01 | 1.00e+00 | 5.80e-01 | 1.00e+00 | 8.46e-01 | 9.94e-01 | 9.84e-01 | 9.83e-01 | 6.55e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.87e-03 | 5.46e-39 | 9.89e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 9.62e-01 | 1.22e-01 | 1.06e-01 | 9.94e-01 | 1.00e+00 | 9.99e-01 | 9.75e-01 | 0.00e+00 |
| 19 | 9.87e-01 | 1.00e+00 | 1.00e+00 | 9.42e-01 | 1.73e-01 | 7.40e-01 | 1.00e+00 | 9.79e-01 | 1.00e+00 | 3.54e-01 | 9.96e-01 | 4.02e-14 | 5.10e-15 | 8.38e-01 | 9.94e-01 | 9.44e-01 | 4.10e-01 | 9.71e-01 | 4.95e-01 | 9.98e-04 | 8.86e-11 | 9.65e-01 | 9.98e-01 | 5.05e-02 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 7.00e-01 | 1.00e+00 | 1.31e-02 | 9.88e-01 | 6.54e-01 | 1.00e+00 | 7.06e-01 | 1.00e+00 | 8.74e-03 | 5.47e-01 | 9.99e-01 | 3.44e-01 | 1.00e+00 | 1.80e-07 | 1.00e+00 | 2.47e-80 | 2.95e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 5.71e-17 | 5.21e-23 | 1.84e-10 | 1.00e+00 | 9.76e-01 | 1.73e-01 | 1.00e+00 | 9.17e-01 | 4.07e-01 | 9.93e-01 | 1.00e+00 | 6.87e-01 | 4.34e-10 | 5.03e-02 | 8.38e-01 | 8.81e-01 | 3.59e-24 | 9.62e-01 | 9.26e-01 | 2.22e-01 | 5.50e-01 | 4.08e-02 | 1.00e+00 | 1.00e+00 | 8.38e-01 | 1.00e+00 | 7.82e-01 | 5.94e-01 | 1.00e+00 | 9.73e-01 | 9.97e-01 | 1.00e+00 | 3.25e-05 | 0.00e+00 | 9.91e-01 | 1.00e+00 | 9.86e-01 | 9.96e-01 | 2.44e-08 | 2.49e-02 | 6.41e-59 | 9.98e-01 | 6.80e-01 | 4.30e-01 | 9.98e-01 | 4.27e-01 | 1.00e+00 | 1.17e-49 | 8.38e-01 | 5.16e-01 | 8.23e-02 | 6.41e-36 | 1.00e+00 | 8.74e-01 | 5.88e-01 | 1.00e+00 | 9.98e-01 | 9.90e-01 | 9.57e-01 | 8.38e-01 | 7.32e-01 | 9.57e-01 | 7.77e-01 | 1.36e-09 | 9.26e-01 | 1.00e+00 | 6.78e-01 | 1.00e+00 | 2.73e-09 | 1.00e+00 | 3.36e-01 | 2.89e-09 | 1.00e+00 | 8.37e-01 | 4.95e-06 | 9.98e-01 | 9.02e-02 | 1.00e+00 | 4.95e-06 | 1.00e+00 | 9.95e-01 | 3.28e-01 | 4.95e-06 | 9.98e-01 | 9.57e-01 | 9.69e-01 | 9.17e-01 | 9.71e-01 | 9.61e-01 | 2.32e-01 | 7.34e-01 | 9.71e-01 | 5.52e-31 | 9.90e-01 | 1.00e+00 | 3.07e-02 | 9.57e-01 | 9.93e-01 | 6.92e-01 | 1.08e-01 | 1.00e+00 | 2.53e-01 | 9.81e-01 | 4.30e-01 | 4.23e-01 | 1.00e+00 | 1.73e-01 | 1.00e+00 | 9.30e-01 | 5.25e-03 | 1.31e-18 | 3.57e-01 | 9.77e-01 | 6.16e-07 | 1.26e-07 | 1.62e-56 | 9.96e-01 | 1.00e+00 | 9.65e-01 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.09e-01 | 2.46e-62 | 9.47e-01 | 1.00e+00 | 9.98e-01 | 2.15e-01 | 1.00e+00 | 9.74e-01 | 1.00e+00 | 9.20e-01 | 1.00e+00 | 1.43e-03 | 9.98e-01 | 9.98e-01 | 1.00e+00 | 8.81e-01 | 8.64e-04 | 1.00e+00 | 9.70e-01 | 7.54e-01 | 9.96e-01 | 9.98e-01 | 1.65e-07 | 9.99e-01 | 9.63e-01 | 9.96e-01 | 6.92e-01 | 3.21e-31 | 1.19e-31 | 1.00e+00 | 4.28e-01 | 1.00e+00 | 9.23e-01 | 9.57e-01 | 9.98e-01 | 9.98e-01 | 9.00e-01 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 4.05e-03 | 4.73e-18 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.81e-01 | 4.21e-01 | 2.02e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.98e-01 | 0.00e+00 |
| 20 | 3.65e-23 | 1.00e+00 | 9.99e-01 | 9.90e-01 | 1.00e+00 | 8.38e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 7.48e-01 | 1.00e+00 | 1.46e-19 | 2.17e-11 | 9.98e-01 | 1.00e+00 | 9.99e-01 | 9.81e-01 | 9.97e-01 | 3.47e-01 | 4.14e-06 | 1.32e-07 | 1.00e+00 | 1.00e+00 | 3.55e-04 | 1.00e+00 | 4.27e-05 | 1.00e+00 | 4.49e-07 | 1.00e+00 | 9.24e-01 | 9.98e-01 | 9.99e-01 | 9.98e-01 | 3.49e-01 | 1.00e+00 | 9.70e-01 | 9.74e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.49e-08 | 1.00e+00 | 9.14e-79 | 3.81e-01 | 0.00e+00 | 1.00e+00 | 9.98e-01 | 3.38e-16 | 5.11e-22 | 2.30e-06 | 1.00e+00 | 9.96e-01 | 0.00e+00 | 9.83e-01 | 1.00e+00 | 7.96e-01 | 1.00e+00 | 1.00e+00 | 9.03e-01 | 4.05e-10 | 1.19e-01 | 9.73e-01 | 9.74e-01 | 1.95e-23 | 7.45e-01 | 9.46e-05 | 9.88e-01 | 8.79e-02 | 7.53e-02 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.96e-01 | 1.00e+00 | 9.78e-01 | 1.00e+00 | 1.99e-01 | 9.76e-01 | 1.00e+00 | 1.75e-07 | 0.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.72e-04 | 1.61e-01 | 1.09e-57 | 1.00e+00 | 9.06e-01 | 9.71e-01 | 1.00e+00 | 8.08e-18 | 1.00e+00 | 9.78e-50 | 9.98e-01 | 1.09e-02 | 4.38e-01 | 2.79e-34 | 1.00e+00 | 9.75e-01 | 8.55e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 9.40e-01 | 3.16e-08 | 9.97e-01 | 1.00e+00 | 3.32e-02 | 9.73e-01 | 3.79e-07 | 1.00e+00 | 3.59e-01 | 1.79e-08 | 1.00e+00 | 1.36e-10 | 1.05e-05 | 1.00e+00 | 9.91e-01 | 9.73e-01 | 1.05e-05 | 1.00e+00 | 9.76e-01 | 6.59e-01 | 1.05e-05 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.98e-01 | 8.99e-01 | 9.97e-01 | 8.89e-01 | 4.31e-01 | 9.95e-01 | 1.59e-66 | 1.00e+00 | 1.00e+00 | 3.12e-01 | 0.00e+00 | 1.00e+00 | 9.98e-01 | 3.94e-01 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 9.99e-01 | 8.79e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 7.68e-05 | 1.73e-19 | 5.35e-01 | 9.61e-01 | 7.72e-04 | 2.83e-04 | 2.79e-52 | 9.99e-01 | 1.00e+00 | 9.19e-01 | 7.00e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.30e-60 | 8.51e-01 | 1.00e+00 | 1.00e+00 | 1.47e-02 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.41e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.22e-04 | 9.73e-01 | 9.95e-01 | 9.81e-01 | 1.00e+00 | 1.00e+00 | 6.51e-01 | 1.00e+00 | 1.00e+00 | 4.27e-01 | 1.05e-05 | 5.30e-30 | 1.00e+00 | 1.00e+00 | 6.55e-01 | 1.00e+00 | 9.48e-01 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 8.55e-01 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.44e-02 | 4.03e-34 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 1.00e+00 | 1.00e+00 | 6.55e-01 | 3.16e-01 | 1.00e+00 | 9.99e-01 | 9.98e-01 | 1.00e+00 | 0.00e+00 |
| 21 | 5.41e-01 | 1.00e+00 | 8.66e-01 | 1.00e+00 | 1.00e+00 | 9.09e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 8.30e-01 | 1.00e+00 | 1.12e-20 | 2.00e-12 | 8.11e-01 | 1.00e+00 | 8.86e-01 | 1.00e+00 | 1.00e+00 | 1.40e-01 | 3.31e-05 | 3.73e-05 | 9.66e-01 | 1.00e+00 | 7.78e-01 | 1.00e+00 | 8.63e-01 | 1.00e+00 | 9.62e-01 | 1.00e+00 | 9.72e-01 | 9.82e-01 | 1.00e+00 | 8.11e-01 | 4.45e-01 | 1.00e+00 | 7.72e-01 | 9.84e-01 | 9.99e-01 | 4.97e-01 | 1.00e+00 | 4.84e-09 | 1.00e+00 | 6.26e-78 | 7.33e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.21e-15 | 4.23e-22 | 1.62e-09 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.47e-01 | 9.97e-01 | 1.00e+00 | 9.32e-01 | 7.87e-10 | 2.40e-01 | 1.00e+00 | 9.84e-01 | 3.04e-22 | 8.37e-01 | 7.02e-01 | 7.60e-04 | 7.97e-06 | 1.03e-01 | 1.00e+00 | 1.00e+00 | 8.11e-01 | 9.98e-01 | 1.00e+00 | 9.58e-01 | 1.00e+00 | 2.53e-01 | 9.98e-01 | 1.00e+00 | 1.14e-02 | 0.00e+00 | 9.98e-01 | 1.00e+00 | 9.91e-01 | 9.98e-01 | 1.88e-04 | 3.84e-02 | 2.21e-56 | 1.00e+00 | 8.33e-01 | 5.13e-07 | 1.00e+00 | 2.18e-01 | 1.00e+00 | 1.29e-47 | 8.11e-01 | 2.01e-01 | 4.46e-01 | 3.64e-34 | 1.00e+00 | 9.59e-01 | 9.79e-01 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 2.80e-01 | 8.11e-01 | 9.85e-01 | 1.00e+00 | 9.60e-01 | 8.59e-08 | 9.78e-01 | 1.00e+00 | 1.11e-03 | 1.00e+00 | 3.16e-06 | 1.00e+00 | 5.65e-01 | 2.21e-07 | 1.00e+00 | 1.42e-10 | 2.15e-05 | 1.00e+00 | 3.27e-03 | 1.00e+00 | 2.15e-05 | 1.00e+00 | 9.37e-01 | 7.42e-01 | 2.15e-05 | 1.00e+00 | 2.80e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.99e-01 | 7.61e-01 | 2.77e-03 | 9.98e-01 | 6.54e-65 | 1.00e+00 | 1.00e+00 | 4.40e-01 | 0.00e+00 | 1.00e+00 | 8.11e-01 | 8.70e-01 | 1.00e+00 | 1.79e-34 | 1.00e+00 | 5.81e-01 | 9.85e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 8.22e-04 | 1.20e-20 | 6.77e-01 | 9.72e-01 | 1.48e-16 | 2.36e-07 | 4.79e-49 | 1.00e+00 | 1.00e+00 | 7.84e-01 | 8.80e-01 | 9.99e-01 | 1.00e+00 | 8.11e-01 | 9.99e-01 | 1.19e-60 | 9.79e-01 | 1.00e+00 | 9.90e-01 | 2.77e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.96e-01 | 1.38e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.64e-01 | 4.77e-14 | 1.00e+00 | 9.97e-01 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 4.69e-07 | 1.00e+00 | 9.62e-01 | 9.53e-01 | 2.15e-05 | 7.93e-30 | 1.00e+00 | 1.00e+00 | 4.38e-01 | 1.00e+00 | 5.26e-01 | 2.80e-01 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.17e-02 | 2.30e-15 | 5.39e-22 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.25e-01 | 3.82e-01 | 1.00e+00 | 9.70e-01 | 8.11e-01 | 9.93e-01 | 0.00e+00 |
| 22 | 4.42e-21 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 6.69e-01 | 8.43e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 6.97e-01 | 1.00e+00 | 6.30e-20 | 5.42e-12 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 9.96e-01 | 9.86e-01 | 1.85e-01 | 6.32e-07 | 6.76e-09 | 1.00e+00 | 1.00e+00 | 8.63e-01 | 1.00e+00 | 8.98e-01 | 1.00e+00 | 9.31e-01 | 1.00e+00 | 1.05e-01 | 9.97e-01 | 9.99e-01 | 1.00e+00 | 3.65e-01 | 1.00e+00 | 4.33e-03 | 9.32e-01 | 9.96e-01 | 8.28e-01 | 1.00e+00 | 3.52e-06 | 1.00e+00 | 5.94e-76 | 7.15e-01 | 0.00e+00 | 8.57e-01 | 8.57e-01 | 3.08e-15 | 2.04e-21 | 5.90e-08 | 1.00e+00 | 9.97e-01 | 6.69e-01 | 1.00e+00 | 9.98e-01 | 7.56e-01 | 1.00e+00 | 1.00e+00 | 8.89e-01 | 1.22e-10 | 1.36e-01 | 1.00e+00 | 9.72e-01 | 9.79e-22 | 8.08e-01 | 6.64e-02 | 4.47e-09 | 2.21e-05 | 1.18e-01 | 9.15e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 6.70e-01 | 1.00e+00 | 2.87e-01 | 9.36e-01 | 1.00e+00 | 2.69e-02 | 0.00e+00 | 9.88e-01 | 9.96e-01 | 9.99e-01 | 1.00e+00 | 2.17e-06 | 1.31e-01 | 1.58e-55 | 1.00e+00 | 9.60e-01 | 5.48e-08 | 1.00e+00 | 1.11e-15 | 1.00e+00 | 6.82e-47 | 1.00e+00 | 1.58e-02 | 2.84e-01 | 5.08e-33 | 1.00e+00 | 9.63e-01 | 7.75e-01 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 8.75e-01 | 1.00e+00 | 9.65e-01 | 1.85e-07 | 7.60e-01 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 3.73e-07 | 1.00e+00 | 2.48e-01 | 5.81e-08 | 8.57e-01 | 6.68e-01 | 4.28e-05 | 1.00e+00 | 1.80e-03 | 1.00e+00 | 4.28e-05 | 1.00e+00 | 9.95e-01 | 6.55e-01 | 4.28e-05 | 1.00e+00 | 6.69e-01 | 9.92e-01 | 1.00e+00 | 9.99e-01 | 9.95e-01 | 7.28e-01 | 1.18e-03 | 9.96e-01 | 4.11e-30 | 9.96e-01 | 1.00e+00 | 3.23e-01 | 0.00e+00 | 9.85e-01 | 9.98e-01 | 3.12e-01 | 1.00e+00 | 9.86e-35 | 1.00e+00 | 8.95e-01 | 9.36e-01 | 1.00e+00 | 6.69e-01 | 1.00e+00 | 9.99e-01 | 1.08e-02 | 4.89e-20 | 3.57e-01 | 9.75e-01 | 2.18e-06 | 1.55e-06 | 1.34e-51 | 9.96e-01 | 1.00e+00 | 9.93e-01 | 8.82e-01 | 9.92e-01 | 1.00e+00 | 4.88e-02 | 9.97e-01 | 3.72e-58 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.07e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.91e-01 | 1.15e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.59e-01 | 4.29e-10 | 1.00e+00 | 9.96e-01 | 9.54e-01 | 1.00e+00 | 1.00e+00 | 1.32e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 4.28e-05 | 1.43e-29 | 4.11e-29 | 1.00e+00 | 8.85e-01 | 1.00e+00 | 9.44e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.74e-01 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 2.66e-02 | 1.97e-15 | 1.33e-21 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.38e-01 | 6.23e-01 | 4.03e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.51e-01 | 0.00e+00 |
| 23 | 3.20e-20 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.16e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.36e-01 | 1.00e+00 | 8.44e-18 | 6.09e-11 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 9.99e-01 | 9.57e-01 | 5.81e-02 | 1.52e-03 | 4.33e-04 | 1.00e+00 | 1.00e+00 | 1.92e-01 | 1.00e+00 | 3.11e-40 | 1.00e+00 | 9.82e-01 | 1.00e+00 | 9.92e-01 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 6.66e-01 | 1.00e+00 | 9.76e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.56e-07 | 1.00e+00 | 1.76e-74 | 4.80e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 2.40e-14 | 2.21e-21 | 2.80e-09 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.55e-01 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 2.97e-09 | 1.04e-01 | 1.00e+00 | 1.00e+00 | 9.97e-22 | 9.03e-01 | 2.00e-02 | 1.68e-03 | 4.23e-05 | 3.70e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.47e-01 | 1.80e-01 | 1.00e+00 | 1.59e-01 | 1.00e+00 | 1.00e+00 | 3.16e-05 | 0.00e+00 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.12e-07 | 2.34e-01 | 5.24e-54 | 1.00e+00 | 9.70e-01 | 2.36e-07 | 1.00e+00 | 4.89e-14 | 1.00e+00 | 1.13e-45 | 1.00e+00 | 9.36e-03 | 7.94e-01 | 8.52e-32 | 1.00e+00 | 1.00e+00 | 6.70e-01 | 1.00e+00 | 9.57e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 5.09e-07 | 9.60e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 2.48e-07 | 1.00e+00 | 3.15e-01 | 8.25e-07 | 1.00e+00 | 4.03e-11 | 8.24e-05 | 9.70e-01 | 9.41e-07 | 1.00e+00 | 8.24e-05 | 1.00e+00 | 1.00e+00 | 9.63e-01 | 8.24e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.72e-01 | 9.83e-01 | 7.88e-01 | 4.61e-03 | 9.97e-01 | 1.93e-62 | 9.81e-01 | 1.00e+00 | 3.70e-01 | 0.00e+00 | 9.98e-01 | 1.00e+00 | 9.77e-01 | 1.00e+00 | 7.68e-33 | 1.00e+00 | 1.00e+00 | 9.82e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.13e-04 | 2.22e-22 | 5.82e-01 | 9.91e-01 | 3.38e-12 | 1.71e-06 | 9.71e-47 | 9.19e-01 | 1.00e+00 | 9.98e-01 | 9.26e-01 | 1.00e+00 | 1.00e+00 | 6.84e-02 | 1.00e+00 | 8.79e-61 | 9.72e-01 | 1.00e+00 | 7.23e-01 | 4.32e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 1.00e+00 | 1.16e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.90e-09 | 1.00e+00 | 6.57e-01 | 9.95e-01 | 9.99e-01 | 1.00e+00 | 3.14e-01 | 1.00e+00 | 1.00e+00 | 1.52e-03 | 8.24e-05 | 1.51e-28 | 1.00e+00 | 1.00e+00 | 7.64e-02 | 1.00e+00 | 1.44e-01 | 1.00e+00 | 1.00e+00 | 9.60e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.33e-02 | 1.61e-13 | 6.45e-21 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.04e-22 | 5.82e-01 | 5.78e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 0.00e+00 |
| 24 | 7.46e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 0.00e+00 | 8.53e-01 | 1.00e+00 | 9.65e-01 | 9.73e-01 | 7.61e-01 | 1.00e+00 | 2.36e-21 | 5.92e-10 | 1.00e+00 | 9.98e-01 | 9.76e-01 | 1.00e+00 | 1.00e+00 | 2.57e-02 | 1.33e-03 | 6.82e-09 | 1.00e+00 | 9.97e-01 | 1.41e-01 | 1.00e+00 | 1.29e-01 | 1.00e+00 | 6.59e-07 | 1.00e+00 | 4.25e-03 | 9.68e-01 | 1.00e+00 | 1.00e+00 | 2.22e-01 | 1.00e+00 | 8.62e-01 | 1.33e-04 | 9.99e-01 | 6.64e-01 | 9.73e-01 | 4.61e-09 | 1.00e+00 | 1.46e-73 | 4.86e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.71e-15 | 2.16e-26 | 1.55e-12 | 1.00e+00 | 9.59e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 5.69e-01 | 8.96e-01 | 1.00e+00 | 9.14e-01 | 8.39e-10 | 1.20e-03 | 1.00e+00 | 9.72e-01 | 5.79e-21 | 9.96e-01 | 5.24e-01 | 5.11e-04 | 8.03e-05 | 7.66e-02 | 1.71e-01 | 9.93e-01 | 1.00e+00 | 9.96e-01 | 9.53e-01 | 2.58e-01 | 9.73e-01 | 2.96e-01 | 9.91e-01 | 1.00e+00 | 3.34e-01 | 0.00e+00 | 4.67e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.54e-06 | 1.07e-01 | 3.35e-53 | 1.00e+00 | 3.43e-01 | 7.82e-07 | 9.97e-01 | 1.41e-01 | 1.00e+00 | 1.60e-44 | 1.00e+00 | 9.02e-02 | 5.93e-01 | 2.40e-32 | 9.73e-01 | 9.99e-01 | 9.68e-01 | 9.73e-01 | 3.11e-01 | 9.81e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.05e-01 | 9.37e-07 | 1.33e-01 | 9.73e-01 | 9.81e-01 | 1.00e+00 | 9.19e-08 | 1.00e+00 | 3.44e-01 | 4.54e-07 | 1.00e+00 | 2.96e-10 | 1.54e-04 | 4.01e-01 | 1.68e-06 | 1.00e+00 | 1.54e-04 | 1.00e+00 | 9.91e-01 | 5.32e-01 | 1.54e-04 | 1.00e+00 | 4.77e-01 | 1.00e+00 | 4.77e-01 | 9.99e-01 | 9.98e-01 | 2.50e-01 | 7.48e-03 | 7.63e-01 | 3.36e-61 | 1.00e+00 | 1.00e+00 | 6.76e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.24e-01 | 1.00e+00 | 7.52e-33 | 1.00e+00 | 7.71e-01 | 9.98e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 4.15e-02 | 1.40e-21 | 4.86e-01 | 9.39e-01 | 3.90e-09 | 2.71e-06 | 8.48e-46 | 1.48e-01 | 9.73e-01 | 1.00e+00 | 9.43e-01 | 9.98e-01 | 9.73e-01 | 1.00e+00 | 9.99e-01 | 1.74e-55 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.91e-02 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 3.87e-01 | 9.71e-01 | 5.51e-04 | 9.97e-01 | 1.00e+00 | 9.73e-01 | 9.83e-01 | 5.47e-03 | 1.00e+00 | 7.81e-03 | 9.88e-01 | 1.00e+00 | 9.98e-01 | 8.09e-07 | 7.71e-01 | 1.00e+00 | 7.50e-01 | 1.54e-04 | 1.89e-27 | 5.19e-01 | 1.00e+00 | 7.24e-03 | 9.73e-01 | 9.86e-01 | 1.00e+00 | 1.00e+00 | 3.33e-01 | 9.93e-01 | 1.00e+00 | 9.73e-01 | 8.03e-01 | 2.30e-02 | 3.05e-32 | 1.07e-20 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.67e-21 | 6.14e-01 | 3.66e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 0.00e+00 |
| 25 | 9.20e-19 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.07e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 7.98e-01 | 1.00e+00 | 2.03e-19 | 5.43e-14 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.92e-01 | 1.00e+00 | 2.25e-01 | 2.56e-05 | 1.82e-07 | 1.00e+00 | 1.00e+00 | 5.93e-03 | 1.00e+00 | 8.57e-04 | 1.00e+00 | 7.43e-06 | 1.00e+00 | 2.09e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 7.89e-01 | 1.00e+00 | 6.32e-02 | 1.79e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.35e-07 | 1.00e+00 | 6.91e-72 | 5.29e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.60e-14 | 4.51e-19 | 3.79e-07 | 1.00e+00 | 9.99e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.11e-01 | 1.00e+00 | 1.00e+00 | 9.21e-01 | 7.33e-09 | 3.57e-01 | 1.00e+00 | 9.84e-01 | 1.01e-19 | 9.96e-01 | 9.05e-04 | 3.49e-07 | 2.69e-05 | 2.26e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.18e-02 | 3.38e-01 | 1.00e+00 | 4.57e-01 | 1.00e+00 | 1.00e+00 | 7.74e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.60e-06 | 5.68e-01 | 6.85e-52 | 1.00e+00 | 8.17e-01 | 1.17e-06 | 1.00e+00 | 1.22e-12 | 1.00e+00 | 2.06e-43 | 1.00e+00 | 8.54e-03 | 7.93e-01 | 2.09e-30 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.56e-01 | 1.40e-06 | 9.99e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.48e-06 | 1.00e+00 | 2.01e-01 | 4.45e-06 | 1.00e+00 | 6.78e-14 | 2.80e-04 | 1.00e+00 | 4.26e-06 | 1.00e+00 | 2.80e-04 | 1.00e+00 | 1.00e+00 | 7.67e-01 | 2.80e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.70e-01 | 2.76e-02 | 9.98e-01 | 7.22e-60 | 1.00e+00 | 1.00e+00 | 7.55e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.40e-01 | 1.00e+00 | 2.26e-31 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 4.34e-03 | 4.69e-23 | 7.21e-01 | 9.99e-01 | 7.52e-13 | 1.05e-07 | 8.58e-50 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.78e-01 | 1.00e+00 | 1.00e+00 | 2.80e-04 | 1.00e+00 | 2.10e-54 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.82e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.72e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 6.94e-14 | 1.00e+00 | 9.98e-01 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 1.24e-05 | 1.00e+00 | 1.00e+00 | 6.56e-01 | 2.80e-04 | 2.69e-26 | 3.56e-27 | 1.00e+00 | 7.64e-01 | 1.00e+00 | 9.69e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.79e-02 | 1.22e-31 | 3.23e-19 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.71e-20 | 8.57e-01 | 5.68e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.44e-01 | 0.00e+00 |
| 26 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 9.27e-01 | 9.89e-01 | 9.99e-01 | 9.89e-01 | 6.78e-01 | 9.99e-01 | 1.94e-18 | 1.28e-12 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.56e-01 | 1.98e-05 | 1.56e-11 | 1.00e+00 | 9.98e-01 | 2.97e-03 | 1.00e+00 | 1.25e-03 | 1.00e+00 | 1.89e-06 | 1.00e+00 | 9.28e-02 | 9.68e-01 | 1.82e-12 | 1.00e+00 | 2.41e-01 | 1.00e+00 | 4.35e-02 | 7.35e-04 | 9.97e-01 | 7.74e-01 | 9.89e-01 | 2.99e-07 | 1.00e+00 | 1.06e-70 | 4.29e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.58e-13 | 1.88e-24 | 1.17e-13 | 1.00e+00 | 9.70e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 5.69e-01 | 9.33e-01 | 1.00e+00 | 9.35e-01 | 2.92e-15 | 1.87e-03 | 1.00e+00 | 9.85e-01 | 6.41e-20 | 9.48e-01 | 1.90e-04 | 4.41e-07 | 3.74e-05 | 4.40e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.90e-01 | 3.36e-01 | 9.89e-01 | 4.95e-01 | 1.00e+00 | 1.00e+00 | 1.71e-03 | 0.00e+00 | 1.96e-08 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.85e-05 | 2.51e-06 | 9.97e-52 | 1.00e+00 | 9.29e-01 | 1.78e-06 | 9.98e-01 | 9.77e-01 | 1.00e+00 | 4.07e-43 | 1.00e+00 | 9.04e-01 | 6.04e-01 | 1.03e-29 | 9.89e-01 | 9.95e-01 | 3.61e-01 | 9.89e-01 | 4.37e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.28e-06 | 4.08e-03 | 9.89e-01 | 9.98e-01 | 1.00e+00 | 4.44e-06 | 1.00e+00 | 1.77e-01 | 6.27e-07 | 1.00e+00 | 2.21e-10 | 4.96e-04 | 5.35e-01 | 2.03e-02 | 1.00e+00 | 4.96e-04 | 1.00e+00 | 1.00e+00 | 6.24e-01 | 4.96e-04 | 9.99e-01 | 6.11e-01 | 9.99e-01 | 6.11e-01 | 9.45e-01 | 9.88e-01 | 2.10e-01 | 9.23e-03 | 8.52e-01 | 4.28e-59 | 1.00e+00 | 1.00e+00 | 6.77e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 3.95e-01 | 1.00e+00 | 4.47e-33 | 1.00e+00 | 8.51e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.08e-03 | 1.56e-22 | 1.80e-01 | 9.63e-01 | 7.15e-09 | 4.59e-05 | 2.84e-50 | 2.17e-01 | 9.89e-01 | 9.99e-01 | 9.66e-01 | 9.97e-01 | 9.89e-01 | 1.60e-01 | 9.99e-01 | 2.09e-53 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 3.97e-02 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 4.74e-01 | 1.00e+00 | 1.32e-07 | 9.98e-01 | 9.99e-01 | 9.89e-01 | 9.84e-01 | 1.05e-09 | 1.00e+00 | 1.80e-02 | 9.89e-01 | 1.00e+00 | 9.99e-01 | 4.38e-01 | 1.00e+00 | 1.00e+00 | 6.32e-01 | 4.96e-04 | 1.11e-25 | 9.86e-29 | 1.00e+00 | 1.03e-02 | 9.89e-01 | 2.72e-01 | 1.00e+00 | 1.00e+00 | 4.62e-01 | 9.88e-01 | 1.00e+00 | 9.89e-01 | 9.07e-01 | 7.28e-02 | 2.41e-30 | 1.89e-19 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.89e-01 | 4.90e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.74e-01 | 0.00e+00 |
| 27 | 4.17e-18 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 9.91e-01 | 9.93e-01 | 8.75e-01 | 1.00e+00 | 7.20e-19 | 3.69e-10 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 3.51e-01 | 4.57e-04 | 8.71e-09 | 1.00e+00 | 1.00e+00 | 9.67e-01 | 1.00e+00 | 4.42e-38 | 1.00e+00 | 5.80e-07 | 1.00e+00 | 9.40e-01 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.97e-02 | 1.00e+00 | 9.64e-01 | 1.05e-04 | 1.00e+00 | 9.99e-01 | 9.93e-01 | 1.37e-06 | 1.00e+00 | 1.16e-69 | 3.98e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 2.83e-13 | 3.26e-23 | 7.65e-06 | 9.93e-01 | 9.99e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.22e-01 | 9.50e-01 | 1.00e+00 | 6.91e-01 | 7.03e-14 | 2.51e-03 | 1.00e+00 | 4.68e-01 | 4.50e-19 | 9.82e-01 | 3.82e-01 | 9.25e-03 | 4.65e-04 | 1.74e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 8.37e-01 | 9.93e-01 | 4.86e-01 | 9.99e-01 | 1.00e+00 | 1.05e-04 | 0.00e+00 | 7.68e-08 | 1.00e+00 | 2.31e-05 | 1.00e+00 | 2.43e-05 | 7.30e-01 | 2.94e-50 | 1.00e+00 | 1.72e-01 | 4.72e-07 | 7.24e-04 | 2.35e-12 | 1.00e+00 | 4.66e-42 | 1.00e+00 | 2.09e-02 | 6.91e-01 | 1.20e-29 | 9.93e-01 | 1.23e-13 | 9.83e-01 | 9.93e-01 | 5.03e-01 | 4.97e-05 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 8.21e-01 | 6.24e-06 | 4.91e-03 | 9.93e-01 | 8.64e-05 | 1.00e+00 | 1.08e-04 | 1.00e+00 | 6.29e-01 | 3.48e-06 | 1.00e+00 | 2.36e-08 | 8.54e-04 | 6.00e-01 | 4.58e-02 | 1.00e+00 | 8.54e-04 | 1.00e+00 | 1.00e+00 | 5.37e-01 | 8.54e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 4.01e-01 | 9.25e-03 | 8.92e-01 | 8.07e-58 | 1.00e+00 | 1.00e+00 | 7.39e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.20e-01 | 1.00e+00 | 1.67e-30 | 1.00e+00 | 9.99e-01 | 8.64e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.32e-02 | 1.27e-20 | 4.89e-01 | 9.86e-01 | 9.66e-15 | 1.43e-07 | 7.71e-46 | 2.65e-01 | 9.93e-01 | 9.99e-01 | 9.77e-01 | 9.99e-01 | 9.93e-01 | 9.06e-01 | 1.00e+00 | 3.95e-54 | 9.99e-01 | 1.00e+00 | 9.97e-01 | 3.54e-02 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 5.12e-01 | 1.00e+00 | 4.79e-05 | 9.29e-04 | 1.00e+00 | 9.93e-01 | 9.93e-01 | 7.22e-12 | 1.00e+00 | 2.63e-02 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 8.78e-06 | 9.91e-01 | 1.00e+00 | 8.58e-03 | 8.54e-04 | 4.42e-25 | 1.00e+00 | 1.00e+00 | 1.41e-02 | 9.93e-01 | 8.47e-01 | 1.00e+00 | 1.00e+00 | 5.28e-01 | 9.98e-01 | 1.00e+00 | 9.93e-01 | 9.99e-01 | 1.12e-01 | 2.77e-29 | 1.19e-18 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.72e-01 | 1.81e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 0.00e+00 |
| 28 | 5.80e-18 | 9.31e-01 | 9.85e-01 | 9.96e-01 | 9.96e-01 | 7.07e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.09e-01 | 1.00e+00 | 3.61e-18 | 2.34e-09 | 1.00e+00 | 9.66e-01 | 1.00e+00 | 9.98e-01 | 9.26e-01 | 3.39e-01 | 3.65e-07 | 4.30e-07 | 1.00e+00 | 9.99e-01 | 9.98e-01 | 9.99e-01 | 4.29e-36 | 1.00e+00 | 3.09e-06 | 1.00e+00 | 8.26e-01 | 9.92e-01 | 9.90e-01 | 9.84e-01 | 2.54e-01 | 1.00e+00 | 2.82e-02 | 1.76e-04 | 7.59e-01 | 1.00e+00 | 9.31e-01 | 7.26e-10 | 1.00e+00 | 2.68e-70 | 4.88e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.07e-12 | 2.27e-25 | 2.61e-06 | 9.31e-01 | 9.96e-01 | 0.00e+00 | 9.85e-01 | 1.00e+00 | 9.29e-01 | 7.10e-01 | 1.00e+00 | 7.98e-01 | 6.42e-10 | 8.13e-06 | 9.84e-01 | 9.41e-01 | 9.38e-19 | 9.61e-01 | 8.83e-02 | 9.90e-04 | 6.33e-06 | 1.02e-01 | 6.22e-03 | 1.00e+00 | 1.00e+00 | 8.84e-01 | 9.86e-01 | 9.15e-01 | 9.31e-01 | 5.73e-01 | 1.00e+00 | 9.31e-01 | 4.81e-06 | 0.00e+00 | 1.48e-01 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 1.95e-06 | 4.40e-01 | 2.20e-49 | 1.00e+00 | 8.32e-01 | 1.54e-06 | 1.00e+00 | 1.19e-11 | 9.99e-01 | 1.19e-42 | 1.00e+00 | 2.20e-02 | 6.07e-01 | 1.85e-28 | 9.31e-01 | 1.00e+00 | 1.38e-01 | 9.31e-01 | 6.46e-02 | 9.53e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.83e-01 | 2.90e-06 | 2.68e-02 | 1.00e+00 | 9.67e-01 | 9.84e-01 | 2.21e-09 | 1.00e+00 | 4.58e-02 | 3.51e-07 | 1.00e+00 | 5.51e-09 | 1.43e-03 | 1.00e+00 | 1.02e-04 | 1.00e+00 | 1.43e-03 | 9.31e-01 | 1.00e+00 | 8.43e-01 | 1.43e-03 | 9.97e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.96e-01 | 9.75e-01 | 5.82e-02 | 1.14e-03 | 5.10e-01 | 7.49e-57 | 1.00e+00 | 1.00e+00 | 6.76e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 5.71e-01 | 1.00e+00 | 4.30e-30 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 2.52e-01 | 1.00e+00 | 7.07e-03 | 2.89e-23 | 5.06e-01 | 8.92e-01 | 9.36e-11 | 6.44e-05 | 1.83e-45 | 8.76e-01 | 9.31e-01 | 9.89e-01 | 9.90e-01 | 9.80e-01 | 9.31e-01 | 9.84e-01 | 1.00e+00 | 3.20e-52 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 7.47e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.29e-01 | 9.20e-01 | 9.70e-05 | 1.00e+00 | 8.77e-02 | 9.31e-01 | 9.81e-01 | 1.46e-12 | 9.84e-01 | 1.62e-05 | 9.89e-01 | 9.97e-01 | 1.00e+00 | 4.40e-08 | 9.96e-01 | 1.00e+00 | 2.02e-02 | 1.43e-03 | 3.72e-26 | 3.80e-01 | 1.00e+00 | 1.39e-02 | 9.31e-01 | 9.42e-01 | 1.00e+00 | 1.00e+00 | 7.55e-02 | 8.82e-01 | 1.00e+00 | 9.31e-01 | 1.00e+00 | 9.70e-02 | 7.23e-32 | 1.11e-18 | 1.00e+00 | 9.84e-01 | 1.00e+00 | 9.97e-01 | 4.81e-01 | 1.85e-01 | 1.00e+00 | 9.99e-01 | 9.84e-01 | 9.99e-01 | 0.00e+00 |
| 29 | 1.69e-21 | 1.00e+00 | 9.61e-01 | 1.00e+00 | 7.82e-01 | 6.50e-01 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 8.27e-01 | 1.00e+00 | 2.67e-17 | 4.75e-11 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 2.92e-04 | 6.77e-09 | 1.80e-08 | 1.00e+00 | 1.00e+00 | 1.12e-02 | 1.00e+00 | 5.07e-03 | 9.31e-01 | 1.26e-05 | 1.00e+00 | 2.46e-01 | 5.72e-06 | 1.00e+00 | 9.51e-01 | 2.72e-01 | 9.92e-01 | 2.04e-02 | 3.51e-06 | 3.05e-04 | 9.07e-01 | 9.90e-01 | 8.41e-06 | 1.00e+00 | 2.23e-66 | 3.76e-01 | 0.00e+00 | 9.90e-01 | 9.90e-01 | 5.29e-12 | 2.06e-21 | 2.08e-10 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.60e-01 | 1.00e+00 | 7.09e-01 | 9.33e-01 | 9.90e-01 | 9.97e-01 | 6.41e-08 | 6.47e-05 | 9.51e-01 | 9.98e-01 | 5.75e-20 | 8.28e-01 | 7.84e-05 | 8.94e-07 | 5.69e-01 | 2.12e-01 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 9.92e-01 | 9.90e-01 | 6.86e-01 | 9.97e-01 | 1.00e+00 | 2.58e-05 | 0.00e+00 | 9.95e-01 | 1.93e-02 | 3.40e-03 | 1.00e+00 | 1.14e-06 | 2.43e-01 | 2.87e-51 | 1.00e+00 | 8.13e-01 | 5.09e-08 | 1.00e+00 | 2.56e-11 | 1.00e+00 | 3.28e-42 | 1.00e+00 | 9.45e-03 | 9.06e-04 | 1.19e-27 | 9.90e-01 | 3.24e-14 | 7.17e-01 | 9.90e-01 | 3.46e-01 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.92e-05 | 6.37e-01 | 9.90e-01 | 4.03e-05 | 9.51e-01 | 4.66e-08 | 1.00e+00 | 3.12e-01 | 6.03e-06 | 9.90e-01 | 1.57e-08 | 2.34e-03 | 4.51e-01 | 5.25e-01 | 3.05e-01 | 2.34e-03 | 1.00e+00 | 1.00e+00 | 7.41e-01 | 2.34e-03 | 1.00e+00 | 7.82e-01 | 9.87e-01 | 7.82e-01 | 9.83e-01 | 9.99e-01 | 7.47e-01 | 5.90e-03 | 8.44e-01 | 3.35e-56 | 9.36e-01 | 1.00e+00 | 6.09e-01 | 0.00e+00 | 9.82e-01 | 0.00e+00 | 8.34e-01 | 1.00e+00 | 8.51e-01 | 2.45e-18 | 9.50e-01 | 9.97e-01 | 1.00e+00 | 7.82e-01 | 1.00e+00 | 9.45e-01 | 2.90e-03 | 3.46e-22 | 5.12e-01 | 8.10e-01 | 1.51e-10 | 9.01e-04 | 1.04e-48 | 6.06e-02 | 9.90e-01 | 9.96e-01 | 9.89e-01 | 6.97e-01 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 3.39e-50 | 9.90e-01 | 3.05e-01 | 1.00e+00 | 9.15e-02 | 1.00e+00 | 2.45e-32 | 9.90e-01 | 3.61e-01 | 1.00e+00 | 4.09e-07 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 9.76e-01 | 7.57e-11 | 9.51e-01 | 4.51e-03 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.23e-06 | 1.00e+00 | 1.00e+00 | 7.43e-01 | 2.34e-03 | 1.31e-30 | 2.52e-27 | 1.00e+00 | 1.82e-03 | 9.90e-01 | 9.81e-01 | 1.00e+00 | 1.00e+00 | 3.72e-01 | 9.59e-01 | 1.00e+00 | 9.90e-01 | 9.37e-01 | 1.04e-01 | 5.40e-29 | 1.02e-18 | 1.00e+00 | 9.51e-01 | 1.00e+00 | 8.60e-01 | 9.61e-01 | 6.35e-01 | 1.00e+00 | 9.95e-01 | 9.51e-01 | 8.27e-01 | 0.00e+00 |
| 30 | 7.82e-17 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.12e-01 | 1.00e+00 | 9.83e-01 | 9.93e-01 | 6.90e-01 | 9.99e-01 | 1.39e-18 | 2.74e-14 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.40e-01 | 2.23e-03 | 9.97e-10 | 1.00e+00 | 9.99e-01 | 3.72e-02 | 1.00e+00 | 7.68e-03 | 1.00e+00 | 4.00e-05 | 1.00e+00 | 2.32e-01 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 1.17e-01 | 1.00e+00 | 1.59e-02 | 3.17e-04 | 9.93e-01 | 5.95e-20 | 9.93e-01 | 4.17e-08 | 1.00e+00 | 5.01e-66 | 2.02e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 3.09e-12 | 1.28e-20 | 1.39e-06 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.68e-01 | 8.76e-01 | 1.00e+00 | 9.98e-01 | 1.16e-09 | 1.35e-03 | 1.00e+00 | 9.99e-01 | 9.07e-18 | 9.85e-01 | 9.58e-04 | 1.01e-06 | 8.08e-05 | 2.41e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.60e-02 | 1.13e-01 | 9.93e-01 | 5.96e-01 | 1.00e+00 | 1.00e+00 | 3.63e-06 | 0.00e+00 | 9.89e-01 | 1.00e+00 | 7.78e-01 | 1.00e+00 | 2.95e-06 | 8.00e-06 | 3.51e-47 | 1.00e+00 | 1.10e-03 | 2.42e-06 | 9.99e-01 | 9.15e-13 | 1.00e+00 | 5.00e-40 | 1.00e+00 | 3.03e-03 | 7.20e-01 | 3.82e-30 | 9.93e-01 | 1.23e-01 | 9.08e-01 | 9.93e-01 | 4.06e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.88e-01 | 9.99e-01 | 9.34e-01 | 2.64e-05 | 9.26e-01 | 9.93e-01 | 3.32e-03 | 1.00e+00 | 3.93e-08 | 1.00e+00 | 4.86e-02 | 1.06e-05 | 1.00e+00 | 1.89e-12 | 3.74e-03 | 5.15e-01 | 3.69e-05 | 1.00e+00 | 3.74e-03 | 1.00e+00 | 6.66e-01 | 5.85e-01 | 3.74e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.37e-01 | 6.09e-02 | 8.57e-01 | 1.45e-54 | 1.00e+00 | 1.00e+00 | 8.67e-01 | 0.00e+00 | 1.00e+00 | 3.74e-03 | 8.26e-01 | 1.00e+00 | 9.06e-29 | 1.00e+00 | 2.17e-13 | 8.64e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 2.40e-02 | 6.88e-24 | 5.24e-01 | 6.89e-01 | 6.24e-08 | 5.11e-05 | 6.28e-47 | 1.33e-01 | 9.93e-01 | 9.15e-01 | 9.86e-01 | 9.91e-01 | 9.93e-01 | 3.63e-01 | 9.98e-01 | 2.98e-49 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.04e-03 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 6.01e-01 | 9.89e-01 | 7.74e-08 | 9.99e-01 | 9.54e-01 | 9.93e-01 | 9.94e-01 | 4.72e-07 | 1.00e+00 | 6.99e-03 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 5.72e-01 | 9.97e-01 | 1.00e+00 | 8.28e-01 | 3.74e-03 | 7.41e-25 | 6.53e-27 | 1.00e+00 | 4.78e-03 | 9.93e-01 | 4.96e-01 | 1.00e+00 | 1.00e+00 | 4.34e-01 | 9.97e-01 | 1.00e+00 | 9.93e-01 | 9.90e-01 | 2.23e-01 | 4.11e-28 | 7.77e-18 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.54e-18 | 8.47e-01 | 5.63e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.84e-01 | 0.00e+00 |
| 31 | 3.52e-01 | 9.99e-01 | 9.97e-01 | 1.00e+00 | 0.00e+00 | 9.12e-01 | 1.00e+00 | 9.95e-01 | 9.99e-01 | 9.14e-01 | 9.99e-01 | 4.17e-20 | 7.51e-15 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.78e-03 | 2.08e-11 | 9.69e-09 | 1.00e+00 | 1.00e+00 | 3.04e-02 | 1.00e+00 | 1.22e-02 | 9.99e-01 | 1.79e-05 | 1.00e+00 | 3.13e-01 | 9.76e-01 | 9.99e-01 | 9.96e-01 | 7.57e-02 | 9.98e-01 | 3.35e-02 | 1.56e-08 | 9.96e-01 | 3.25e-19 | 9.99e-01 | 1.89e-06 | 1.00e+00 | 1.65e-64 | 9.52e-02 | 0.00e+00 | 9.96e-01 | 9.96e-01 | 1.21e-11 | 3.43e-21 | 4.00e-11 | 9.99e-01 | 1.00e+00 | 0.00e+00 | 9.97e-01 | 1.00e+00 | 9.43e-01 | 9.88e-01 | 9.96e-01 | 9.65e-01 | 1.09e-13 | 1.94e-02 | 4.24e-01 | 7.02e-01 | 1.32e-18 | 5.58e-01 | 4.84e-04 | 1.82e-06 | 3.98e-04 | 3.74e-01 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.51e-01 | 9.99e-01 | 6.85e-01 | 9.99e-01 | 1.00e+00 | 5.93e-07 | 0.00e+00 | 6.34e-07 | 1.00e+00 | 2.94e-03 | 1.00e+00 | 3.80e-05 | 1.04e-05 | 6.41e-46 | 1.00e+00 | 2.51e-03 | 3.11e-07 | 1.00e+00 | 8.17e-01 | 1.00e+00 | 1.02e-39 | 1.00e+00 | 3.44e-01 | 7.79e-01 | 5.59e-27 | 9.99e-01 | 6.49e-12 | 9.83e-01 | 9.99e-01 | 7.44e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 9.87e-01 | 9.99e-01 | 9.99e-01 | 6.18e-05 | 1.18e-04 | 9.99e-01 | 3.36e-04 | 4.24e-01 | 1.04e-06 | 1.00e+00 | 4.92e-01 | 3.96e-05 | 9.96e-01 | 2.98e-08 | 5.83e-03 | 8.18e-01 | 6.87e-05 | 9.77e-01 | 5.83e-03 | 1.00e+00 | 1.00e+00 | 8.00e-01 | 5.83e-03 | 1.00e+00 | 9.77e-01 | 1.00e+00 | 9.77e-01 | 1.00e+00 | 1.00e+00 | 6.02e-01 | 1.22e-02 | 9.69e-01 | 6.15e-54 | 9.99e-01 | 1.00e+00 | 5.55e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.49e-01 | 1.00e+00 | 8.68e-28 | 9.99e-01 | 1.53e-13 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 6.21e-03 | 4.70e-23 | 6.87e-01 | 8.71e-01 | 3.75e-10 | 9.72e-05 | 4.46e-46 | 3.13e-01 | 9.99e-01 | 9.02e-01 | 9.94e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 3.77e-48 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.70e-04 | 1.00e+00 | 9.98e-01 | 9.99e-01 | 6.89e-01 | 1.00e+00 | 2.33e-08 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 | 6.07e-10 | 9.96e-01 | 9.46e-02 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 7.68e-06 | 1.00e+00 | 1.00e+00 | 7.79e-01 | 5.83e-03 | 1.77e-25 | 3.99e-25 | 1.00e+00 | 6.23e-02 | 9.99e-01 | 9.88e-01 | 9.96e-01 | 1.00e+00 | 7.65e-01 | 9.90e-01 | 9.96e-01 | 9.99e-01 | 9.97e-01 | 2.40e-01 | 2.63e-27 | 2.08e-17 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.53e-18 | 9.63e-01 | 7.06e-02 | 9.96e-01 | 1.00e+00 | 9.96e-01 | 8.96e-01 | 0.00e+00 |
| 32 | 1.63e-15 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 0.00e+00 | 8.78e-01 | 1.00e+00 | 9.99e-01 | 4.86e-01 | 9.94e-01 | 1.00e+00 | 5.20e-20 | 1.27e-10 | 9.98e-01 | 9.99e-01 | 9.92e-01 | 1.00e+00 | 9.96e-01 | 3.50e-02 | 5.54e-06 | 2.05e-06 | 1.00e+00 | 1.00e+00 | 1.02e-02 | 9.95e-01 | 1.75e-02 | 9.84e-01 | 1.39e-06 | 1.00e+00 | 5.97e-01 | 4.74e-01 | 1.07e-11 | 9.98e-01 | 1.58e-02 | 9.45e-01 | 9.90e-01 | 1.22e-03 | 9.99e-01 | 6.38e-02 | 1.00e+00 | 3.35e-10 | 1.00e+00 | 1.43e-63 | 1.02e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 5.50e-11 | 3.13e-19 | 8.34e-14 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.67e-01 | 2.96e-02 | 8.97e-01 | 7.94e-01 | 2.78e-09 | 3.77e-01 | 9.98e-01 | 9.98e-01 | 8.74e-18 | 9.94e-01 | 1.75e-03 | 6.17e-03 | 4.53e-04 | 5.05e-01 | 3.48e-25 | 1.00e+00 | 9.98e-01 | 9.86e-01 | 8.33e-01 | 1.03e-03 | 1.00e+00 | 7.80e-01 | 9.97e-01 | 1.00e+00 | 1.04e-04 | 0.00e+00 | 9.94e-01 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 2.73e-04 | 2.46e-07 | 7.68e-45 | 1.00e+00 | 4.49e-01 | 2.95e-05 | 1.00e+00 | 2.62e-13 | 1.00e+00 | 1.62e-37 | 9.98e-01 | 1.77e-04 | 1.92e-01 | 5.81e-26 | 1.00e+00 | 1.00e+00 | 5.19e-01 | 1.00e+00 | 1.00e+00 | 8.50e-01 | 1.00e+00 | 9.98e-01 | 9.96e-01 | 9.97e-01 | 9.90e-01 | 1.61e-04 | 2.44e-05 | 4.86e-01 | 9.66e-01 | 1.00e+00 | 3.06e-06 | 1.00e+00 | 4.10e-01 | 7.90e-05 | 1.00e+00 | 2.09e-08 | 8.89e-03 | 2.02e-05 | 1.31e-05 | 1.00e+00 | 8.89e-03 | 1.00e+00 | 9.98e-01 | 9.15e-01 | 8.89e-03 | 1.00e+00 | 8.89e-03 | 9.99e-01 | 8.89e-03 | 9.75e-01 | 1.00e+00 | 1.84e-01 | 5.70e-03 | 8.43e-03 | 1.86e-53 | 6.11e-01 | 1.00e+00 | 2.98e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.12e-01 | 1.00e+00 | 5.02e-27 | 1.00e+00 | 1.24e-01 | 9.99e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 2.93e-02 | 1.29e-19 | 8.61e-01 | 9.54e-01 | 1.07e-09 | 7.23e-07 | 2.31e-46 | 3.06e-07 | 1.00e+00 | 9.91e-01 | 9.98e-01 | 9.66e-01 | 4.86e-01 | 1.00e+00 | 9.90e-01 | 4.02e-47 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.72e-03 | 1.00e+00 | 1.00e+00 | 4.86e-01 | 9.46e-01 | 1.00e+00 | 1.70e-06 | 1.00e+00 | 9.37e-01 | 1.00e+00 | 9.98e-01 | 4.46e-13 | 1.00e+00 | 8.77e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.77e-06 | 9.96e-01 | 1.00e+00 | 6.76e-01 | 8.89e-03 | 1.64e-22 | 8.79e-01 | 1.00e+00 | 6.73e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 3.09e-06 | 8.01e-01 | 1.00e+00 | 1.00e+00 | 2.01e-01 | 6.93e-02 | 1.05e-26 | 1.93e-16 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.16e-18 | 7.87e-01 | 1.77e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.94e-01 | 0.00e+00 |
| 33 | 5.01e-19 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 8.77e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.77e-01 | 1.00e+00 | 5.44e-17 | 9.42e-13 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 3.00e-03 | 3.22e-06 | 2.12e-08 | 1.00e+00 | 1.00e+00 | 4.36e-01 | 1.00e+00 | 4.93e-32 | 9.98e-01 | 3.73e-05 | 1.00e+00 | 3.48e-01 | 7.02e-05 | 1.00e+00 | 1.00e+00 | 9.57e-01 | 1.00e+00 | 1.57e-01 | 3.33e-04 | 1.79e-03 | 1.00e+00 | 1.00e+00 | 3.64e-07 | 1.00e+00 | 2.99e-61 | 3.97e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 4.64e-10 | 4.95e-18 | 9.79e-10 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.62e-32 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 9.14e-01 | 2.35e-11 | 3.57e-01 | 1.00e+00 | 1.00e+00 | 4.67e-16 | 1.00e+00 | 1.26e-01 | 2.04e-05 | 8.86e-07 | 2.52e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 5.26e-01 | 1.00e+00 | 5.98e-01 | 2.78e-02 | 1.00e+00 | 1.09e-03 | 0.00e+00 | 2.82e-06 | 3.48e-18 | 1.00e+00 | 1.00e+00 | 6.52e-06 | 8.50e-01 | 6.94e-47 | 1.00e+00 | 9.93e-01 | 1.11e-08 | 1.00e+00 | 2.54e-11 | 1.00e+00 | 4.22e-78 | 1.00e+00 | 1.18e-05 | 3.00e-03 | 2.00e-24 | 1.00e+00 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 4.26e-04 | 2.90e-09 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 5.24e-10 | 1.00e+00 | 2.68e-01 | 1.92e-04 | 1.00e+00 | 3.19e-07 | 1.32e-02 | 9.96e-01 | 2.99e-04 | 5.48e-01 | 1.32e-02 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 1.32e-02 | 1.00e+00 | 1.32e-02 | 9.99e-01 | 1.32e-02 | 1.00e+00 | 1.00e+00 | 4.03e-01 | 4.93e-03 | 1.00e+00 | 1.16e-50 | 9.96e-01 | 1.00e+00 | 9.65e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.45e-01 | 1.00e+00 | 5.79e-26 | 5.62e-19 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.29e-01 | 8.57e-21 | 3.62e-03 | 4.12e-05 | 5.95e-16 | 1.47e-06 | 6.73e-44 | 1.51e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 4.00e-01 | 1.00e+00 | 5.48e-01 | 1.00e+00 | 3.15e-49 | 1.00e+00 | 5.48e-01 | 9.83e-01 | 5.90e-02 | 1.00e+00 | 2.69e-29 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.78e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.66e-12 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 6.23e-05 | 1.00e+00 | 1.00e+00 | 3.69e-02 | 1.32e-02 | 3.90e-35 | 2.64e-21 | 1.00e+00 | 9.77e-01 | 1.00e+00 | 6.99e-01 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 9.82e-04 | 1.00e+00 | 1.00e+00 | 2.15e-01 | 2.01e-01 | 2.03e-26 | 3.85e-16 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 2.80e-16 | 9.82e-01 | 9.36e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 0.00e+00 |
| 34 | 3.73e-16 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.65e-01 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 9.89e-01 | 1.00e+00 | 5.21e-20 | 4.62e-12 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 9.99e-01 | 1.00e+00 | 2.59e-03 | 9.20e-07 | 5.24e-08 | 1.00e+00 | 1.00e+00 | 7.39e-01 | 1.00e+00 | 4.82e-31 | 1.00e+00 | 3.26e-05 | 1.00e+00 | 4.12e-01 | 1.32e-04 | 3.44e-09 | 1.00e+00 | 9.93e-02 | 1.00e+00 | 5.19e-02 | 3.67e-05 | 1.01e-01 | 1.00e+00 | 1.00e+00 | 9.70e-08 | 1.00e+00 | 3.30e-61 | 3.67e-06 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.25e-10 | 5.94e-25 | 1.71e-05 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.88e-01 | 1.27e-05 | 1.00e+00 | 6.40e-01 | 3.61e-12 | 8.43e-02 | 9.44e-01 | 9.99e-01 | 1.06e-15 | 9.86e-01 | 1.14e-02 | 5.99e-05 | 1.77e-05 | 4.19e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 | 1.25e-02 | 1.00e+00 | 7.83e-01 | 1.00e+00 | 1.00e+00 | 1.26e-04 | 0.00e+00 | 3.83e-05 | 2.43e-01 | 1.05e-03 | 1.00e+00 | 8.02e-06 | 7.02e-03 | 5.19e-44 | 1.00e+00 | 9.76e-01 | 3.70e-08 | 1.00e+00 | 4.90e-10 | 1.00e+00 | 1.24e-35 | 1.00e+00 | 6.79e-02 | 5.42e-03 | 4.36e-24 | 1.00e+00 | 9.63e-11 | 9.35e-01 | 1.00e+00 | 1.00e+00 | 4.71e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.74e-01 | 9.88e-01 | 3.51e-04 | 1.02e-01 | 1.00e+00 | 3.47e-08 | 1.00e+00 | 3.49e-07 | 1.00e+00 | 8.91e-02 | 6.80e-05 | 1.00e+00 | 7.59e-07 | 1.93e-02 | 9.98e-01 | 1.67e-04 | 9.93e-01 | 1.93e-02 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.93e-02 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 9.56e-01 | 1.00e+00 | 5.63e-01 | 8.45e-03 | 2.07e-08 | 1.27e-50 | 9.02e-01 | 1.00e+00 | 1.82e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 8.37e-01 | 1.00e+00 | 4.05e-26 | 6.11e-18 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.02e-02 | 3.55e-18 | 9.33e-01 | 9.82e-01 | 1.06e-14 | 2.59e-06 | 4.54e-45 | 1.18e-01 | 1.00e+00 | 1.00e+00 | 9.60e-01 | 1.00e+00 | 1.93e-02 | 6.08e-01 | 1.00e+00 | 3.36e-48 | 1.00e+00 | 9.93e-01 | 9.84e-01 | 3.20e-02 | 9.44e-01 | 9.54e-30 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 3.49e-06 | 1.00e+00 | 8.97e-01 | 1.00e+00 | 1.00e+00 | 1.60e-12 | 1.00e+00 | 6.25e-01 | 9.87e-01 | 1.00e+00 | 1.00e+00 | 1.93e-05 | 9.90e-01 | 1.00e+00 | 8.78e-02 | 1.93e-02 | 5.93e-26 | 1.76e-21 | 1.00e+00 | 9.19e-01 | 1.00e+00 | 7.13e-01 | 1.00e+00 | 1.00e+00 | 8.71e-44 | 8.15e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.68e-02 | 1.96e-26 | 4.65e-15 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.81e-17 | 9.33e-01 | 2.45e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 0.00e+00 |
| 35 | 6.81e-14 | 9.96e-01 | 1.00e+00 | 9.99e-01 | 0.00e+00 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.44e-01 | 1.00e+00 | 6.36e-20 | 1.66e-09 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.16e-02 | 2.17e-05 | 5.25e-07 | 1.00e+00 | 1.00e+00 | 1.38e-01 | 1.00e+00 | 5.47e-02 | 1.00e+00 | 1.93e-04 | 1.00e+00 | 4.38e-01 | 3.30e-04 | 9.98e-01 | 1.00e+00 | 6.35e-01 | 1.00e+00 | 3.75e-02 | 2.66e-06 | 9.98e-01 | 1.48e-16 | 9.96e-01 | 4.09e-07 | 9.96e-01 | 1.15e-61 | 3.79e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 3.04e-10 | 4.27e-24 | 5.46e-07 | 9.96e-01 | 8.33e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 2.00e-01 | 1.00e+00 | 9.57e-01 | 1.33e-13 | 3.05e-04 | 1.00e+00 | 9.45e-01 | 4.77e-15 | 8.92e-01 | 5.67e-03 | 2.21e-05 | 5.04e-09 | 7.11e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.40e-01 | 9.30e-02 | 6.50e-01 | 9.96e-01 | 6.48e-01 | 1.00e+00 | 1.00e+00 | 1.22e-04 | 0.00e+00 | 6.53e-05 | 2.74e-01 | 1.14e-02 | 1.00e+00 | 5.44e-06 | 2.58e-05 | 3.42e-42 | 1.00e+00 | 1.30e-01 | 7.14e-06 | 1.00e+00 | 4.63e-10 | 1.00e+00 | 8.82e-35 | 1.00e+00 | 7.57e-02 | 5.71e-02 | 1.38e-24 | 9.96e-01 | 5.32e-11 | 7.83e-01 | 9.96e-01 | 9.33e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.64e-01 | 4.47e-04 | 2.21e-01 | 1.00e+00 | 6.20e-04 | 1.00e+00 | 7.68e-08 | 1.00e+00 | 2.77e-01 | 2.47e-06 | 1.00e+00 | 9.82e-10 | 2.75e-02 | 1.00e+00 | 6.92e-04 | 1.00e+00 | 2.75e-02 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 2.75e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.24e-01 | 3.12e-03 | 7.85e-03 | 1.43e-48 | 9.89e-01 | 1.00e+00 | 7.25e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 3.99e-01 | 9.96e-01 | 1.11e-27 | 3.82e-17 | 7.15e-13 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.68e-01 | 2.10e-25 | 8.20e-01 | 1.00e+00 | 3.00e-11 | 1.43e-07 | 7.02e-43 | 9.03e-01 | 9.96e-01 | 9.98e-01 | 9.99e-01 | 1.00e+00 | 6.66e-01 | 2.75e-02 | 9.74e-01 | 8.49e-44 | 3.31e-06 | 1.00e+00 | 1.00e+00 | 6.49e-05 | 1.00e+00 | 1.10e-30 | 1.00e+00 | 9.59e-01 | 1.00e+00 | 5.32e-05 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 9.99e-01 | 5.29e-10 | 1.00e+00 | 4.87e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.83e-05 | 1.00e+00 | 1.00e+00 | 9.46e-01 | 2.75e-02 | 5.17e-28 | 9.56e-20 | 1.00e+00 | 8.32e-01 | 9.96e-01 | 2.06e-01 | 1.00e+00 | 1.00e+00 | 2.32e-05 | 9.84e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 2.53e-01 | 1.65e-23 | 1.15e-20 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 3.26e-15 | 2.73e-01 | 2.00e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 0.00e+00 |
| 36 | 7.60e-14 | 9.71e-01 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.32e-01 | 1.00e+00 | 1.00e+00 | 7.20e-01 | 9.80e-01 | 1.00e+00 | 4.12e-16 | 3.24e-11 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.78e-03 | 8.75e-07 | 6.71e-09 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.73e-30 | 9.95e-01 | 1.15e-03 | 1.00e+00 | 3.65e-01 | 1.29e-04 | 1.19e-08 | 1.00e+00 | 2.18e-02 | 1.00e+00 | 7.87e-02 | 1.64e-03 | 1.00e+00 | 6.02e-16 | 1.00e+00 | 3.86e-08 | 1.00e+00 | 2.45e-60 | 3.50e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 3.96e-09 | 1.71e-17 | 1.41e-09 | 9.71e-01 | 1.00e+00 | 0.00e+00 | 6.92e-34 | 1.00e+00 | 9.67e-01 | 2.49e-01 | 1.00e+00 | 1.00e+00 | 7.52e-09 | 6.18e-01 | 1.00e+00 | 1.00e+00 | 4.30e-15 | 9.96e-01 | 7.10e-03 | 5.68e-05 | 1.15e-05 | 7.65e-01 | 1.07e-01 | 1.00e+00 | 1.00e+00 | 6.00e-01 | 9.91e-01 | 1.65e-01 | 1.00e+00 | 9.40e-01 | 1.16e-01 | 1.00e+00 | 7.90e-05 | 0.00e+00 | 4.74e-01 | 2.65e-01 | 1.00e+00 | 1.00e+00 | 6.84e-06 | 3.94e-16 | 8.57e-41 | 1.00e+00 | 9.61e-02 | 3.91e-06 | 1.00e+00 | 4.24e-09 | 1.00e+00 | 2.62e-73 | 1.00e+00 | 4.29e-03 | 2.00e-02 | 6.23e-23 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.21e-03 | 6.79e-12 | 7.20e-01 | 9.98e-01 | 1.00e+00 | 1.24e-11 | 1.00e+00 | 2.28e-02 | 4.61e-04 | 1.00e+00 | 8.73e-08 | 3.84e-02 | 2.24e-04 | 1.03e-03 | 1.00e+00 | 3.84e-02 | 1.00e+00 | 1.00e+00 | 9.59e-01 | 3.84e-02 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.07e-01 | 2.98e-02 | 1.17e-02 | 1.05e-47 | 9.87e-01 | 1.00e+00 | 9.19e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.68e-01 | 1.00e+00 | 1.22e-25 | 2.27e-15 | 1.30e-10 | 1.81e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 9.97e-01 | 2.50e-03 | 2.80e-21 | 4.65e-02 | 2.02e-05 | 4.35e-09 | 7.46e-08 | 1.48e-42 | 1.63e-16 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 7.20e-01 | 1.00e+00 | 9.86e-01 | 1.02e-42 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.99e-03 | 1.00e+00 | 7.05e-30 | 7.20e-01 | 1.00e+00 | 1.00e+00 | 2.98e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.81e-11 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.19e-04 | 1.00e+00 | 1.00e+00 | 7.37e-02 | 3.84e-02 | 9.69e-34 | 5.67e-24 | 1.00e+00 | 9.27e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.35e-05 | 1.17e-04 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 3.37e-01 | 1.15e-23 | 1.35e-15 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 4.99e-15 | 4.02e-01 | 1.74e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.81e-01 | 0.00e+00 |
| 37 | 4.25e-13 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.00e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.12e-01 | 1.00e+00 | 5.13e-17 | 2.47e-09 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.63e-03 | 1.30e-05 | 2.85e-06 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 4.37e-30 | 1.00e+00 | 2.29e-03 | 1.00e+00 | 2.69e-01 | 9.60e-08 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 1.28e-01 | 4.29e-03 | 9.97e-01 | 1.04e-15 | 1.00e+00 | 2.99e-06 | 1.00e+00 | 7.57e-57 | 1.04e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 7.16e-09 | 8.81e-17 | 8.04e-10 | 1.00e+00 | 4.27e-01 | 0.00e+00 | 6.14e-29 | 1.00e+00 | 9.47e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.10e-12 | 5.88e-01 | 1.00e+00 | 1.00e+00 | 6.33e-14 | 9.53e-01 | 2.06e-03 | 3.19e-05 | 3.39e-04 | 6.64e-01 | 6.03e-22 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 2.03e-01 | 4.96e-01 | 1.00e+00 | 8.94e-01 | 2.28e-01 | 1.00e+00 | 1.12e-11 | 0.00e+00 | 1.00e-04 | 3.63e-01 | 3.07e-02 | 1.00e+00 | 1.25e-06 | 3.92e-04 | 8.48e-40 | 1.00e+00 | 1.78e-01 | 3.55e-06 | 1.00e+00 | 4.58e-10 | 1.00e+00 | 3.84e-72 | 1.00e+00 | 4.29e-02 | 6.22e-02 | 5.45e-23 | 1.00e+00 | 1.02e-09 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.71e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.03e-03 | 2.27e-13 | 1.00e+00 | 4.41e-05 | 1.00e+00 | 1.32e-06 | 1.00e+00 | 5.05e-01 | 2.00e-03 | 1.00e+00 | 2.59e-10 | 5.26e-02 | 1.00e+00 | 1.45e-03 | 1.00e+00 | 5.26e-02 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 5.26e-02 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 2.20e-01 | 6.62e-02 | 1.00e+00 | 1.66e-46 | 1.00e+00 | 1.00e+00 | 8.60e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.37e-01 | 1.00e+00 | 1.74e-23 | 1.43e-15 | 3.95e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.51e-01 | 3.37e-27 | 1.10e-03 | 1.21e-05 | 9.03e-12 | 1.17e-09 | 1.62e-44 | 3.65e-01 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.26e-02 | 1.00e+00 | 5.98e-42 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.34e-04 | 1.00e+00 | 5.79e-27 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.19e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.04e-11 | 0.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.18e-04 | 1.00e+00 | 1.00e+00 | 1.62e-01 | 5.26e-02 | 3.23e-31 | 5.55e-20 | 1.00e+00 | 2.74e-01 | 1.00e+00 | 2.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.35e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.60e-01 | 2.95e-23 | 5.65e-14 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 2.20e-14 | 9.15e-01 | 9.69e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 0.00e+00 |
| 38 | 4.55e-14 | 9.99e-01 | 9.92e-01 | 1.00e+00 | 0.00e+00 | 8.65e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 2.49e-18 | 2.71e-13 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 6.21e-04 | 5.34e-08 | 3.06e-07 | 1.00e+00 | 1.00e+00 | 3.57e-16 | 1.00e+00 | 1.38e-28 | 1.00e+00 | 9.22e-04 | 1.00e+00 | 2.68e-01 | 1.57e-03 | 2.27e-08 | 9.87e-01 | 7.86e-01 | 1.00e+00 | 3.66e-02 | 1.08e-04 | 1.35e-01 | 1.00e+00 | 1.00e+00 | 3.43e-08 | 1.00e+00 | 6.71e-57 | 2.48e-06 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 2.00e-08 | 1.81e-17 | 9.01e-07 | 9.99e-01 | 1.00e+00 | 0.00e+00 | 9.91e-01 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 1.21e-11 | 1.90e-01 | 1.00e+00 | 1.00e+00 | 5.30e-15 | 7.84e-01 | 9.22e-04 | 1.99e-04 | 4.62e-04 | 3.36e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 8.45e-01 | 2.75e-02 | 1.00e+00 | 9.68e-01 | 1.00e+00 | 9.99e-01 | 1.08e-11 | 0.00e+00 | 4.93e-05 | 2.62e-01 | 1.33e-02 | 1.00e+00 | 1.40e-06 | 5.74e-03 | 2.10e-40 | 1.00e+00 | 9.96e-01 | 2.55e-07 | 1.00e+00 | 1.19e-09 | 1.00e+00 | 1.42e-34 | 1.00e+00 | 2.21e-02 | 1.99e-02 | 1.71e-22 | 1.00e+00 | 4.67e-10 | 5.50e-01 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.84e-01 | 1.50e-03 | 7.47e-02 | 1.00e+00 | 4.60e-05 | 9.87e-01 | 1.54e-07 | 1.00e+00 | 2.62e-01 | 8.47e-04 | 1.00e+00 | 6.90e-07 | 7.05e-02 | 9.95e-01 | 9.22e-04 | 9.99e-01 | 7.05e-02 | 9.99e-01 | 9.86e-01 | 9.66e-01 | 7.05e-02 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 8.02e-01 | 6.39e-03 | 1.00e+00 | 6.84e-46 | 9.85e-01 | 1.00e+00 | 9.35e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.72e-01 | 1.00e+00 | 1.00e-23 | 2.21e-14 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.87e-01 | 1.00e+00 | 1.85e-01 | 8.26e-21 | 3.62e-01 | 4.43e-01 | 4.05e-11 | 5.94e-09 | 1.44e-45 | 8.58e-01 | 1.00e+00 | 8.99e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 2.08e-41 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 2.31e-01 | 1.00e+00 | 1.68e-27 | 1.00e+00 | 9.47e-01 | 9.99e-01 | 7.80e-06 | 1.00e+00 | 5.73e-01 | 1.00e+00 | 9.96e-01 | 5.95e-15 | 9.87e-01 | 7.01e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.14e-04 | 9.99e-01 | 1.00e+00 | 2.40e-02 | 7.05e-02 | 8.45e-25 | 5.28e-20 | 1.00e+00 | 9.73e-03 | 1.00e+00 | 1.03e-08 | 9.99e-01 | 1.00e+00 | 9.92e-01 | 9.93e-01 | 9.87e-01 | 1.00e+00 | 9.78e-01 | 2.52e-01 | 6.75e-24 | 8.05e-14 | 1.00e+00 | 9.87e-01 | 1.00e+00 | 7.56e-16 | 9.70e-01 | 8.98e-01 | 1.00e+00 | 9.99e-01 | 9.87e-01 | 5.43e-02 | 0.00e+00 |
| 39 | 7.17e-12 | 1.00e+00 | 1.50e-01 | 1.00e+00 | 0.00e+00 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.02e-01 | 1.00e+00 | 5.17e-16 | 2.95e-11 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.63e-05 | 1.34e-05 | 1.90e-05 | 6.62e-01 | 1.00e+00 | 1.01e-17 | 2.61e-01 | 2.15e-28 | 1.00e+00 | 1.30e-03 | 1.00e+00 | 3.41e-02 | 1.35e-03 | 9.59e-01 | 9.28e-02 | 8.00e-01 | 9.96e-01 | 2.29e-02 | 1.69e-04 | 1.00e+00 | 1.43e-14 | 1.00e+00 | 3.43e-06 | 1.00e+00 | 3.67e-54 | 3.42e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 3.22e-08 | 2.56e-18 | 5.04e-10 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 8.85e-01 | 1.00e+00 | 9.94e-01 | 6.48e-01 | 9.91e-01 | 1.00e+00 | 2.45e-12 | 6.24e-02 | 1.00e+00 | 1.00e+00 | 9.33e-14 | 4.64e-03 | 5.11e-03 | 2.52e-05 | 5.60e-03 | 7.62e-01 | 9.96e-01 | 1.00e+00 | 9.28e-02 | 1.00e+00 | 3.69e-01 | 5.40e-01 | 1.00e+00 | 9.69e-01 | 9.99e-01 | 1.00e+00 | 2.56e-06 | 0.00e+00 | 1.32e-04 | 1.21e-01 | 4.45e-02 | 9.92e-01 | 6.39e-06 | 6.89e-05 | 6.63e-38 | 1.00e+00 | 5.02e-02 | 3.82e-07 | 1.00e+00 | 2.99e-08 | 1.00e+00 | 4.51e-35 | 9.28e-02 | 2.76e-05 | 1.23e-01 | 1.39e-24 | 1.00e+00 | 1.17e-08 | 1.00e+00 | 1.00e+00 | 8.76e-01 | 1.00e+00 | 0.00e+00 | 9.28e-02 | 3.65e-01 | 1.00e+00 | 1.00e+00 | 3.95e-03 | 4.88e-06 | 1.00e+00 | 7.10e-14 | 1.00e+00 | 7.17e-07 | 1.00e+00 | 3.36e-01 | 2.12e-03 | 1.00e+00 | 1.32e-08 | 9.28e-02 | 9.25e-01 | 8.79e-04 | 1.00e+00 | 9.28e-02 | 1.00e+00 | 1.00e+00 | 9.76e-01 | 9.28e-02 | 1.00e+00 | 8.51e-01 | 1.00e+00 | 8.51e-01 | 9.87e-01 | 1.00e+00 | 8.14e-01 | 5.87e-03 | 9.95e-01 | 1.63e-45 | 1.00e+00 | 1.00e+00 | 5.71e-01 | 0.00e+00 | 7.68e-01 | 0.00e+00 | 9.99e-01 | 1.00e+00 | 7.22e-22 | 3.20e-13 | 9.58e-10 | 7.67e-03 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 3.02e-17 | 1.54e-07 | 8.14e-23 | 9.10e-01 | 9.50e-01 | 9.96e-11 | 1.91e-10 | 8.40e-50 | 3.73e-01 | 1.00e+00 | 6.22e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 1.03e-39 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.12e-03 | 1.00e+00 | 2.51e-25 | 1.00e+00 | 8.60e-01 | 8.59e-01 | 1.34e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.54e-09 | 8.51e-01 | 1.35e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.27e-03 | 6.01e-03 | 1.00e+00 | 2.74e-01 | 9.28e-02 | 1.16e-27 | 1.39e-20 | 1.00e+00 | 7.23e-02 | 1.00e+00 | 9.90e-01 | 0.00e+00 | 1.00e+00 | 8.90e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 9.81e-01 | 6.21e-01 | 4.91e-22 | 6.48e-13 | 1.00e+00 | 8.51e-01 | 1.00e+00 | 2.50e-19 | 9.98e-01 | 9.68e-01 | 9.91e-01 | 8.34e-01 | 9.28e-02 | 1.00e+00 | 0.00e+00 |
| 40 | 4.10e-12 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.45e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 2.84e-17 | 2.18e-11 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 3.03e-04 | 1.98e-08 | 5.74e-06 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 8.57e-29 | 1.00e+00 | 2.14e-06 | 9.94e-01 | 2.37e-01 | 7.15e-03 | 9.88e-01 | 1.00e+00 | 8.49e-01 | 1.00e+00 | 5.61e-02 | 1.22e-09 | 1.00e+00 | 5.85e-14 | 1.00e+00 | 2.38e-05 | 1.00e+00 | 1.32e-52 | 3.64e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.43e-08 | 2.03e-17 | 4.82e-11 | 1.00e+00 | 9.95e-01 | 0.00e+00 | 8.03e-28 | 1.00e+00 | 9.52e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.04e-12 | 5.64e-02 | 9.94e-01 | 1.00e+00 | 1.05e-12 | 9.76e-01 | 3.16e-02 | 2.24e-04 | 1.33e-08 | 7.55e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.25e-01 | 8.42e-01 | 1.00e+00 | 9.83e-01 | 1.00e+00 | 1.00e+00 | 1.24e-02 | 0.00e+00 | 1.99e-04 | 4.34e-01 | 1.92e-02 | 1.00e+00 | 1.28e-06 | 1.30e-04 | 2.10e-37 | 9.99e-01 | 1.15e-02 | 1.43e-06 | 1.00e+00 | 6.95e-09 | 1.00e+00 | 8.06e-69 | 1.00e+00 | 1.91e-02 | 1.90e-01 | 2.26e-23 | 1.00e+00 | 2.72e-08 | 1.00e+00 | 1.00e+00 | 9.04e-01 | 9.86e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.70e-03 | 1.65e-02 | 1.00e+00 | 7.65e-08 | 9.94e-01 | 3.38e-07 | 1.00e+00 | 1.02e-01 | 3.57e-03 | 1.00e+00 | 1.53e-09 | 1.20e-01 | 9.44e-01 | 4.06e-03 | 1.00e+00 | 1.20e-01 | 1.00e+00 | 9.99e-01 | 9.91e-01 | 1.20e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 6.85e-01 | 1.77e-01 | 9.95e-01 | 1.28e-43 | 1.00e+00 | 9.94e-01 | 9.78e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.98e-01 | 1.00e+00 | 3.84e-22 | 1.21e-12 | 3.02e-10 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.69e-01 | 1.75e-21 | 9.62e-01 | 7.71e-06 | 2.50e-12 | 3.85e-07 | 1.89e-41 | 6.17e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.75e-01 | 1.00e+00 | 8.83e-01 | 1.00e+00 | 7.99e-42 | 1.34e-04 | 1.00e+00 | 9.99e-01 | 3.34e-03 | 1.00e+00 | 2.66e-24 | 1.00e+00 | 9.31e-01 | 1.00e+00 | 4.55e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.93e-15 | 0.00e+00 | 1.70e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.18e-03 | 1.39e-02 | 1.00e+00 | 1.77e-01 | 1.20e-01 | 7.25e-40 | 6.85e-20 | 1.00e+00 | 1.26e-01 | 1.00e+00 | 9.27e-01 | 1.00e+00 | 9.99e-01 | 9.16e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.15e-08 | 4.65e-01 | 2.58e-21 | 1.28e-18 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 3.14e-13 | 8.75e-01 | 9.66e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 0.00e+00 |
showPValues(2) # Venues Depth 2
P-Values thresholded at 0.05 for features: Venues Depth 2
| ATM | Adult Boutique | Afghan Restaurant | African Restaurant | Airport | American Restaurant | Animal Shelter | Antique Shop | Aquarium | Art Gallery | Art Museum | Arts & Crafts Store | Asian Restaurant | Athletics & Sports | Auto Dealership | Automotive Shop | BBQ Joint | Baby Store | Badminton Court | Bagel Shop | Bakery | Bank | Bar | Baseball Field | Basketball Court | Basketball Stadium | Beach | Beer Bar | Beer Store | Belgian Restaurant | Big Box Store | Bike Shop | Bistro | Boat or Ferry | Bookstore | Boutique | Bowling Alley | Breakfast Spot | Brewery | Bridal Shop | Bridge | Bubble Tea Shop | Building | Burger Joint | Burrito Place | Bus Line | Bus Station | Bus Stop | Business Service | Butcher | Cafeteria | Café | Cajun / Creole Restaurant | Candy Store | Caribbean Restaurant | Carpet Store | Castle | Cemetery | Cheese Shop | Chinese Restaurant | Chiropractor | Chocolate Shop | Church | Clothing Store | Cocktail Bar | Coffee Shop | College Arts Building | College Gym | College Quad | College Rec Center | College Stadium | Comfort Food Restaurant | Comic Shop | Community Center | Concert Hall | Convenience Store | Cosmetics Shop | Costume Shop | Creperie | Cuban Restaurant | Cupcake Shop | Curling Ice | Dance Studio | Deli / Bodega | Department Store | Dessert Shop | Diner | Discount Store | Distribution Center | Dive Bar | Dog Run | Donut Shop | Dry Cleaner | Dumpling Restaurant | Eastern European Restaurant | Electronics Store | Elementary School | Escape Room | Ethiopian Restaurant | Event Space | Falafel Restaurant | Farm | Farmers Market | Fast Food Restaurant | Field | Filipino Restaurant | Fish & Chips Shop | Fish Market | Flea Market | Flower Shop | Food | Food & Drink Shop | Food Court | Food Truck | Fountain | French Restaurant | Fried Chicken Joint | Frozen Yogurt Shop | Fruit & Vegetable Store | Furniture / Home Store | Gaming Cafe | Garden | Garden Center | Gas Station | Gastropub | Gay Bar | General Entertainment | General Travel | German Restaurant | Gift Shop | Gluten-free Restaurant | Golf Course | Golf Driving Range | Gourmet Shop | Greek Restaurant | Grocery Store | Gym / Fitness Center | Halal Restaurant | Harbor / Marina | Hardware Store | Health Food Store | Historic Site | History Museum | Hobby Shop | Hockey Arena | Hookah Bar | Hostel | Hotel | Hotel Bar | Hotpot Restaurant | IT Services | Ice Cream Shop | Indian Chinese Restaurant | Indian Restaurant | Indie Movie Theater | Indoor Play Area | Intersection | Irish Pub | Italian Restaurant | Japanese Restaurant | Jazz Club | Jewelry Store | Jewish Restaurant | Juice Bar | Karaoke Bar | Kitchen Supply Store | Korean Restaurant | Lake | Latin American Restaurant | Laundry Service | Leather Goods Store | Library | Light Rail Station | Lighting Store | Lingerie Store | Liquor Store | Lounge | Luggage Store | Malay Restaurant | Market | Massage Studio | Medical Center | Medical Supply Store | Mediterranean Restaurant | Men's Store | Metro Station | Mexican Restaurant | Middle Eastern Restaurant | Miscellaneous Shop | Mobile Phone Shop | Modern European Restaurant | Molecular Gastronomy Restaurant | Monument / Landmark | Moroccan Restaurant | Movie Theater | Moving Target | Museum | Music Store | Music Venue | Nail Salon | Neighborhood | New American Restaurant | Nightclub | Noodle House | Office | Opera House | Optical Shop | Organic Grocery | Other Great Outdoors | Other Nightlife | Other Repair Shop | Outdoor Supply Store | Paintball Field | Pakistani Restaurant | Paper / Office Supplies Store | Park | Performing Arts Venue | Persian Restaurant | Pet Store | Pharmacy | Pizza Place | Platform | Playground | Plaza | Poke Place | Polish Restaurant | Pool | Pool Hall | Portuguese Restaurant | Poutine Place | Pub | Racetrack | Record Shop | Recreation Center | Rental Car Location | Rental Service | Residential Building (Apartment / Condo) | Restaurant | River | Rock Climbing Spot | Rock Club | Roof Deck | Sake Bar | Salad Place | Salon / Barbershop | Sandwich Place | Scenic Lookout | School | Science Museum | Sculpture Garden | Seafood Restaurant | Shoe Store | Shopping Mall | Shopping Plaza | Skate Park | Skating Rink | Smoke Shop | Smoothie Shop | Snack Place | Soccer Field | Soccer Stadium | Soup Place | South American Restaurant | Spa | Spanish Restaurant | Speakeasy | Sporting Goods Shop | Sports Bar | Sports Club | Sri Lankan Restaurant | Stadium | Stationery Store | Steakhouse | Strip Club | Supermarket | Supplement Shop | Swiss Restaurant | Taco Place | Tailor Shop | Tanning Salon | Tapas Restaurant | Tea Room | Tech Startup | Tennis Court | Tex-Mex Restaurant | Thai Restaurant | Theater | Theme Park Ride / Attraction | Theme Restaurant | Thrift / Vintage Store | Tibetan Restaurant | Toy / Game Store | Trail | Train Station | Tram Station | Transportation Service | Tree | Turkish Restaurant | Vegetarian / Vegan Restaurant | Veterinarian | Video Game Store | Video Store | Vietnamese Restaurant | Volleyball Court | Warehouse Store | Waste Facility | Wine Bar | Wine Shop | Wings Joint | Women's Store | Zoo Exhibit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2 | 8.92e-01 | 9.19e-01 | 9.02e-01 | 9.19e-01 | 9.19e-01 | 6.51e-01 | 9.19e-01 | 8.64e-01 | 9.19e-01 | 7.56e-01 | 9.19e-01 | 8.19e-01 | 6.93e-01 | 7.03e-01 | 9.19e-01 | 8.59e-01 | 7.26e-01 | 8.59e-01 | 8.85e-01 | 7.78e-01 | 3.68e-01 | 4.34e-01 | 6.29e-01 | 8.53e-01 | 9.19e-01 | 8.85e-01 | 8.88e-01 | 7.24e-01 | 6.15e-01 | 8.85e-01 | 8.94e-01 | 8.67e-01 | 8.90e-01 | 8.67e-01 | 7.24e-01 | 8.89e-01 | 9.19e-01 | 6.18e-01 | 7.57e-01 | 8.73e-01 | 9.19e-01 | 7.37e-01 | 8.96e-01 | 5.80e-01 | 7.59e-01 | 7.80e-01 | 8.63e-01 | 8.27e-01 | 8.84e-01 | 8.17e-01 | 9.19e-01 | 3.99e-01 | 9.19e-01 | 9.13e-01 | 6.65e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 8.12e-01 | 6.26e-01 | 9.19e-01 | 8.42e-01 | 8.89e-01 | 7.76e-01 | 7.91e-01 | 1.30e-01 | 9.19e-01 | 8.62e-01 | 9.19e-01 | 9.03e-01 | 9.19e-01 | 7.68e-01 | 8.59e-01 | 9.19e-01 | 8.20e-01 | 6.85e-01 | 7.56e-01 | 9.19e-01 | 8.40e-01 | 8.63e-01 | 8.85e-01 | 8.64e-01 | 8.25e-01 | 8.48e-01 | 7.54e-01 | 6.38e-01 | 6.53e-01 | 6.65e-01 | 8.43e-01 | 8.85e-01 | 8.89e-01 | 8.40e-01 | 9.19e-01 | 8.83e-01 | 8.39e-01 | 6.78e-01 | 9.19e-01 | 9.19e-01 | 8.40e-01 | 8.60e-01 | 8.04e-01 | 9.19e-01 | 7.50e-01 | 4.93e-01 | 9.19e-01 | 8.55e-01 | 7.64e-01 | 7.84e-01 | 8.42e-01 | 7.97e-01 | 8.85e-01 | 8.04e-01 | 8.06e-01 | 8.99e-01 | 8.87e-01 | 7.29e-01 | 6.72e-01 | 8.22e-01 | 8.85e-01 | 7.66e-01 | 8.85e-01 | 3.97e-71 | 9.19e-01 | 6.24e-01 | 6.59e-01 | 8.81e-01 | 8.85e-01 | 9.19e-01 | 8.62e-01 | 7.50e-01 | 9.19e-01 | 8.85e-01 | 9.19e-01 | 7.72e-01 | 6.90e-01 | 4.29e-01 | 4.43e-01 | 9.19e-01 | 9.19e-01 | 8.32e-01 | 8.70e-01 | 8.28e-01 | 8.90e-01 | 8.27e-01 | 8.72e-01 | 8.90e-01 | 9.19e-01 | 7.37e-01 | 8.60e-01 | 9.19e-01 | 9.19e-01 | 6.78e-01 | 9.19e-01 | 6.57e-01 | 8.90e-01 | 9.19e-01 | 6.27e-01 | 8.61e-01 | 4.84e-01 | 4.35e-01 | 8.87e-01 | 8.05e-01 | 9.19e-01 | 7.54e-01 | 8.06e-01 | 9.19e-01 | 8.14e-01 | 8.88e-01 | 8.01e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 8.23e-01 | 9.19e-01 | 8.69e-01 | 6.05e-01 | 7.62e-01 | 9.19e-01 | 8.85e-01 | 8.86e-01 | 9.19e-01 | 8.61e-01 | 9.19e-01 | 6.84e-01 | 8.40e-01 | 8.10e-01 | 6.18e-01 | 6.51e-01 | 8.60e-01 | 8.82e-01 | 8.44e-01 | 9.19e-01 | 8.85e-01 | 8.86e-01 | 7.50e-01 | 9.19e-01 | 8.61e-01 | 9.19e-01 | 8.20e-01 | 9.19e-01 | 8.62e-01 | 7.71e-01 | 8.76e-01 | 8.28e-01 | 9.01e-01 | 9.19e-01 | 8.52e-01 | 9.19e-01 | 8.49e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 8.85e-01 | 8.91e-01 | 7.83e-01 | 4.50e-01 | 8.22e-01 | 8.85e-01 | 6.92e-01 | 4.42e-01 | 3.15e-01 | 9.19e-01 | 1.11e-45 | 8.13e-01 | 8.86e-01 | 9.19e-01 | 7.97e-01 | 8.59e-01 | 8.67e-01 | 9.19e-01 | 5.70e-01 | 9.19e-01 | 8.21e-01 | 9.05e-01 | 8.69e-01 | 9.19e-01 | 8.85e-01 | 3.60e-01 | 9.19e-01 | 9.02e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 8.24e-01 | 8.11e-01 | 3.38e-01 | 8.85e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 6.83e-01 | 7.71e-01 | 7.09e-01 | 9.19e-01 | 9.19e-01 | 7.10e-01 | 8.59e-01 | 7.86e-01 | 8.60e-01 | 8.64e-01 | 8.94e-01 | 8.86e-01 | 8.32e-01 | 6.90e-01 | 8.94e-01 | 8.94e-01 | 7.76e-01 | 7.88e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 7.38e-01 | 9.19e-01 | 6.06e-01 | 8.67e-01 | 9.19e-01 | 8.91e-01 | 8.87e-01 | 8.86e-01 | 8.67e-01 | 7.99e-01 | 8.86e-01 | 8.40e-01 | 9.19e-01 | 5.76e-01 | 7.33e-01 | 9.19e-01 | 9.19e-01 | 8.36e-01 | 9.19e-01 | 7.17e-01 | 7.82e-01 | 8.63e-01 | 9.19e-01 | 9.19e-01 | 9.19e-01 | 8.85e-01 | 6.88e-01 | 9.19e-01 | 8.41e-01 | 9.19e-01 | 7.30e-01 | 9.19e-01 | 8.86e-01 | 9.19e-01 | 8.23e-01 | 8.86e-01 | 7.62e-01 | 8.64e-01 | 9.19e-01 |
| 3 | 9.53e-01 | 9.73e-01 | 9.61e-01 | 9.73e-01 | 9.73e-01 | 5.87e-01 | 9.73e-01 | 9.26e-01 | 9.73e-01 | 7.78e-01 | 9.73e-01 | 8.72e-01 | 6.66e-01 | 6.85e-01 | 9.73e-01 | 9.21e-01 | 7.27e-01 | 9.22e-01 | 9.47e-01 | 8.13e-01 | 1.17e-01 | 2.00e-01 | 5.43e-01 | 9.14e-01 | 9.73e-01 | 9.47e-01 | 9.50e-01 | 7.22e-01 | 5.17e-01 | 9.47e-01 | 9.55e-01 | 9.30e-01 | 9.51e-01 | 9.30e-01 | 7.23e-01 | 9.51e-01 | 9.73e-01 | 5.22e-01 | 7.79e-01 | 9.36e-01 | 9.73e-01 | 7.46e-01 | 9.57e-01 | 4.48e-01 | 7.83e-01 | 8.16e-01 | 9.26e-01 | 8.84e-01 | 9.46e-01 | 8.70e-01 | 9.73e-01 | 7.02e-01 | 9.73e-01 | 2.93e-06 | 6.14e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 8.64e-01 | 5.38e-01 | 9.73e-01 | 9.02e-01 | 9.50e-01 | 8.09e-01 | 8.34e-01 | 1.84e-03 | 9.73e-01 | 9.25e-01 | 9.73e-01 | 9.62e-01 | 9.73e-01 | 7.98e-01 | 9.22e-01 | 9.73e-01 | 8.73e-01 | 6.51e-01 | 7.79e-01 | 9.73e-01 | 8.99e-01 | 9.26e-01 | 9.47e-01 | 9.26e-01 | 8.80e-01 | 9.09e-01 | 7.74e-01 | 5.62e-01 | 5.90e-01 | 6.13e-01 | 9.03e-01 | 9.47e-01 | 9.51e-01 | 9.00e-01 | 9.73e-01 | 9.46e-01 | 8.98e-01 | 6.38e-01 | 9.73e-01 | 9.73e-01 | 8.99e-01 | 9.23e-01 | 8.52e-01 | 9.73e-01 | 7.67e-01 | 2.91e-01 | 9.73e-01 | 9.16e-01 | 7.91e-01 | 8.22e-01 | 9.02e-01 | 8.42e-01 | 9.47e-01 | 8.52e-01 | 8.54e-01 | 4.27e-06 | 9.49e-01 | 7.31e-01 | 6.27e-01 | 8.76e-01 | 9.47e-01 | 7.93e-01 | 9.47e-01 | 2.52e-69 | 9.73e-01 | 5.35e-01 | 6.02e-01 | 9.44e-01 | 9.47e-01 | 9.73e-01 | 9.24e-01 | 7.68e-01 | 9.73e-01 | 1.39e-05 | 9.73e-01 | 8.04e-01 | 6.60e-01 | 3.22e-02 | 7.31e-01 | 9.73e-01 | 9.73e-01 | 8.90e-01 | 9.33e-01 | 8.84e-01 | 9.51e-01 | 8.84e-01 | 9.34e-01 | 9.51e-01 | 9.73e-01 | 7.46e-01 | 9.22e-01 | 9.73e-01 | 9.73e-01 | 6.39e-01 | 9.73e-01 | 5.99e-01 | 9.52e-01 | 9.73e-01 | 3.79e-02 | 9.23e-01 | 2.77e-01 | 5.98e-01 | 9.49e-01 | 8.54e-01 | 9.73e-01 | 7.74e-01 | 8.54e-01 | 9.73e-01 | 8.65e-01 | 9.50e-01 | 8.47e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 8.79e-01 | 9.73e-01 | 9.32e-01 | 4.98e-01 | 7.88e-01 | 9.73e-01 | 9.47e-01 | 9.48e-01 | 9.73e-01 | 9.23e-01 | 9.73e-01 | 6.49e-01 | 9.00e-01 | 8.60e-01 | 5.22e-01 | 5.87e-01 | 9.22e-01 | 2.00e-05 | 9.04e-01 | 9.73e-01 | 9.47e-01 | 9.48e-01 | 7.69e-01 | 9.73e-01 | 9.23e-01 | 9.73e-01 | 8.74e-01 | 9.73e-01 | 9.24e-01 | 8.03e-01 | 9.39e-01 | 8.85e-01 | 9.61e-01 | 9.73e-01 | 9.14e-01 | 9.73e-01 | 9.10e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 9.47e-01 | 9.52e-01 | 8.21e-01 | 3.22e-19 | 8.77e-01 | 9.47e-01 | 6.64e-01 | 2.11e-01 | 6.81e-02 | 9.73e-01 | 6.16e-52 | 8.65e-01 | 9.48e-01 | 9.73e-01 | 8.42e-01 | 9.21e-01 | 9.29e-01 | 9.73e-01 | 4.29e-01 | 9.73e-01 | 8.76e-01 | 9.64e-01 | 9.32e-01 | 9.73e-01 | 9.47e-01 | 1.09e-01 | 9.73e-01 | 9.61e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 8.80e-01 | 8.62e-01 | 6.23e-01 | 9.47e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 6.47e-01 | 8.01e-01 | 6.96e-01 | 9.73e-01 | 9.73e-01 | 6.98e-01 | 9.22e-01 | 8.26e-01 | 9.23e-01 | 9.27e-01 | 9.55e-01 | 9.48e-01 | 8.90e-01 | 6.61e-01 | 9.55e-01 | 9.55e-01 | 8.10e-01 | 8.29e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 7.47e-01 | 9.73e-01 | 4.99e-01 | 9.30e-01 | 9.73e-01 | 9.52e-01 | 9.49e-01 | 9.48e-01 | 9.30e-01 | 8.45e-01 | 9.48e-01 | 8.99e-01 | 9.73e-01 | 6.99e-02 | 7.38e-01 | 9.73e-01 | 9.73e-01 | 8.95e-01 | 9.73e-01 | 7.11e-01 | 1.79e-02 | 9.25e-01 | 9.73e-01 | 9.73e-01 | 9.73e-01 | 9.47e-01 | 6.57e-01 | 9.73e-01 | 9.01e-01 | 9.73e-01 | 7.33e-01 | 9.73e-01 | 9.48e-01 | 9.73e-01 | 8.78e-01 | 9.48e-01 | 7.88e-01 | 9.27e-01 | 9.73e-01 |
| 4 | 9.74e-01 | 9.89e-01 | 9.80e-01 | 9.89e-01 | 1.92e-02 | 4.72e-01 | 9.89e-01 | 9.48e-01 | 9.89e-01 | 7.58e-01 | 9.89e-01 | 8.87e-01 | 5.90e-01 | 6.18e-01 | 9.89e-01 | 9.43e-01 | 6.81e-01 | 9.44e-01 | 9.69e-01 | 8.08e-01 | 2.09e-01 | 7.20e-01 | 4.10e-01 | 9.36e-01 | 9.89e-01 | 9.68e-01 | 9.71e-01 | 6.75e-01 | 3.74e-01 | 9.68e-01 | 9.75e-01 | 9.52e-01 | 9.72e-01 | 9.52e-01 | 6.76e-01 | 9.71e-01 | 9.89e-01 | 3.80e-01 | 7.59e-01 | 9.58e-01 | 9.89e-01 | 7.11e-01 | 9.76e-01 | 2.83e-01 | 7.64e-01 | 8.12e-01 | 9.48e-01 | 9.01e-01 | 9.67e-01 | 8.84e-01 | 9.89e-01 | 5.72e-01 | 9.89e-01 | 2.10e-02 | 7.30e-02 | 9.89e-01 | 9.89e-01 | 9.89e-01 | 8.76e-01 | 8.39e-01 | 9.89e-01 | 9.22e-01 | 9.71e-01 | 8.03e-01 | 8.36e-01 | 1.97e-01 | 9.89e-01 | 9.47e-01 | 9.89e-01 | 9.81e-01 | 9.89e-01 | 7.86e-01 | 9.44e-01 | 9.89e-01 | 8.88e-01 | 8.25e-01 | 7.59e-01 | 9.89e-01 | 9.19e-01 | 9.48e-01 | 9.68e-01 | 9.48e-01 | 8.97e-01 | 9.30e-01 | 7.52e-01 | 4.36e-01 | 4.77e-01 | 3.77e-01 | 9.24e-01 | 9.69e-01 | 9.71e-01 | 9.20e-01 | 9.89e-01 | 9.67e-01 | 4.96e-01 | 9.32e-01 | 9.89e-01 | 9.89e-01 | 9.19e-01 | 9.45e-01 | 8.61e-01 | 9.89e-01 | 7.42e-01 | 3.72e-01 | 9.89e-01 | 9.38e-01 | 7.76e-01 | 8.21e-01 | 9.23e-01 | 8.47e-01 | 9.68e-01 | 8.61e-01 | 8.64e-01 | 2.61e-02 | 9.70e-01 | 6.88e-01 | 5.31e-01 | 8.92e-01 | 9.68e-01 | 7.80e-01 | 9.68e-01 | 9.92e-68 | 9.89e-01 | 8.23e-01 | 4.95e-01 | 9.65e-01 | 9.69e-01 | 9.89e-01 | 9.47e-01 | 7.43e-01 | 9.89e-01 | 2.84e-03 | 9.89e-01 | 7.95e-01 | 5.81e-01 | 1.76e-01 | 3.85e-01 | 9.89e-01 | 9.89e-01 | 9.09e-01 | 9.55e-01 | 9.01e-01 | 9.72e-01 | 4.65e-14 | 9.56e-01 | 9.72e-01 | 9.89e-01 | 7.10e-01 | 9.44e-01 | 9.89e-01 | 9.89e-01 | 5.49e-01 | 9.89e-01 | 4.90e-01 | 9.72e-01 | 9.89e-01 | 8.76e-02 | 9.45e-01 | 4.44e-01 | 5.42e-01 | 9.70e-01 | 8.63e-01 | 9.89e-01 | 2.64e-02 | 8.64e-01 | 9.89e-01 | 8.78e-01 | 9.71e-01 | 3.66e-03 | 9.89e-01 | 9.89e-01 | 9.89e-01 | 8.95e-01 | 9.89e-01 | 9.54e-01 | 3.47e-01 | 7.72e-01 | 9.89e-01 | 9.68e-01 | 9.69e-01 | 9.89e-01 | 9.45e-01 | 9.89e-01 | 5.65e-01 | 9.20e-01 | 8.71e-01 | 3.80e-01 | 4.73e-01 | 9.44e-01 | 1.22e-03 | 9.25e-01 | 9.89e-01 | 9.68e-01 | 9.69e-01 | 7.44e-01 | 1.92e-02 | 9.45e-01 | 9.89e-01 | 8.89e-01 | 9.89e-01 | 9.47e-01 | 7.94e-01 | 4.37e-02 | 9.02e-01 | 9.79e-01 | 9.89e-01 | 9.36e-01 | 9.89e-01 | 9.31e-01 | 9.89e-01 | 1.92e-02 | 9.89e-01 | 9.68e-01 | 9.73e-01 | 6.85e-07 | 5.85e-21 | 8.92e-01 | 9.68e-01 | 5.86e-01 | 6.22e-01 | 4.83e-01 | 9.89e-01 | 4.30e-45 | 8.78e-01 | 9.69e-01 | 9.89e-01 | 8.47e-01 | 9.43e-01 | 9.52e-01 | 9.89e-01 | 2.60e-01 | 9.89e-01 | 8.91e-01 | 9.82e-01 | 9.54e-01 | 9.89e-01 | 9.69e-01 | 2.17e-02 | 9.89e-01 | 9.80e-01 | 9.89e-01 | 9.89e-01 | 9.89e-01 | 8.96e-01 | 8.73e-01 | 5.90e-01 | 9.68e-01 | 9.89e-01 | 9.89e-01 | 9.89e-01 | 5.62e-01 | 7.92e-01 | 7.69e-01 | 9.89e-01 | 9.89e-01 | 9.55e-01 | 9.44e-01 | 8.26e-01 | 9.45e-01 | 9.49e-01 | 9.75e-01 | 9.69e-01 | 9.09e-01 | 2.18e-02 | 9.75e-01 | 9.75e-01 | 8.03e-01 | 8.30e-01 | 9.89e-01 | 9.89e-01 | 9.89e-01 | 9.89e-01 | 7.12e-01 | 9.89e-01 | 5.16e-01 | 9.52e-01 | 9.89e-01 | 9.72e-01 | 9.70e-01 | 9.69e-01 | 9.52e-01 | 8.51e-01 | 9.69e-01 | 9.20e-01 | 9.89e-01 | 8.28e-01 | 6.99e-01 | 9.89e-01 | 9.89e-01 | 9.14e-01 | 9.89e-01 | 6.58e-01 | 5.40e-02 | 9.47e-01 | 9.89e-01 | 9.89e-01 | 9.89e-01 | 5.84e-02 | 5.77e-01 | 9.89e-01 | 9.22e-01 | 9.89e-01 | 1.17e-01 | 9.89e-01 | 9.69e-01 | 9.89e-01 | 8.94e-01 | 9.69e-01 | 7.72e-01 | 9.49e-01 | 0.00e+00 |
| 5 | 4.53e-01 | 9.65e-01 | 9.31e-01 | 9.65e-01 | 7.26e-01 | 7.91e-01 | 9.65e-01 | 7.96e-01 | 9.65e-01 | 2.33e-01 | 9.65e-01 | 5.58e-01 | 6.69e-01 | 8.60e-01 | 9.65e-01 | 7.74e-01 | 3.00e-01 | 7.76e-01 | 9.93e-01 | 3.35e-01 | 1.80e-01 | 1.25e-01 | 4.18e-02 | 5.69e-01 | 7.26e-01 | 8.81e-01 | 8.92e-01 | 1.22e-01 | 8.20e-01 | 8.81e-01 | 4.70e-01 | 9.84e-01 | 8.59e-01 | 9.97e-01 | 7.47e-01 | 8.94e-01 | 9.65e-01 | 1.43e-01 | 3.77e-01 | 5.66e-01 | 9.65e-01 | 6.95e-01 | 8.67e-01 | 4.51e-02 | 2.45e-01 | 8.28e-01 | 9.94e-01 | 7.98e-01 | 9.09e-01 | 5.50e-01 | 9.65e-01 | 1.00e-04 | 9.65e-01 | 1.02e-12 | 6.68e-02 | 9.65e-01 | 9.65e-01 | 9.65e-01 | 5.22e-01 | 5.13e-04 | 9.65e-01 | 6.87e-01 | 8.93e-01 | 3.22e-01 | 4.05e-01 | 1.23e-06 | 9.65e-01 | 7.89e-01 | 9.65e-01 | 8.13e-01 | 9.65e-01 | 2.87e-01 | 7.77e-01 | 7.26e-01 | 5.64e-01 | 9.31e-01 | 2.35e-01 | 9.65e-01 | 6.76e-01 | 7.95e-01 | 8.81e-01 | 7.96e-01 | 5.94e-01 | 9.00e-01 | 9.73e-01 | 5.62e-02 | 3.55e-01 | 3.99e-01 | 6.95e-01 | 8.82e-01 | 8.94e-01 | 6.79e-01 | 9.65e-01 | 9.93e-01 | 9.63e-01 | 8.98e-01 | 9.65e-01 | 9.65e-01 | 6.76e-01 | 9.95e-01 | 4.75e-01 | 9.65e-01 | 2.07e-01 | 5.13e-03 | 9.65e-01 | 9.60e-01 | 6.64e-01 | 3.64e-01 | 9.95e-01 | 9.69e-01 | 8.82e-01 | 9.80e-01 | 6.36e-01 | 2.14e-12 | 8.88e-01 | 9.21e-01 | 9.21e-01 | 9.70e-01 | 8.81e-01 | 8.04e-01 | 8.81e-01 | 1.61e-66 | 9.65e-01 | 9.38e-04 | 2.06e-02 | 8.68e-01 | 8.82e-01 | 9.65e-01 | 9.67e-01 | 2.09e-01 | 9.65e-01 | 3.38e-11 | 7.26e-01 | 8.84e-01 | 1.32e-01 | 1.11e-01 | 3.24e-02 | 7.26e-01 | 9.65e-01 | 9.92e-01 | 9.68e-01 | 6.09e-01 | 8.97e-01 | 9.35e-01 | 9.96e-01 | 8.97e-01 | 9.65e-01 | 2.55e-01 | 7.78e-01 | 7.26e-01 | 9.65e-01 | 4.35e-01 | 9.65e-01 | 8.13e-01 | 8.97e-01 | 9.65e-01 | 1.53e-05 | 7.83e-01 | 2.99e-01 | 1.72e-03 | 8.88e-01 | 4.82e-01 | 9.65e-01 | 8.62e-01 | 9.59e-01 | 9.65e-01 | 9.60e-01 | 8.93e-01 | 3.92e-01 | 7.26e-01 | 9.65e-01 | 9.65e-01 | 9.75e-01 | 9.65e-01 | 8.19e-01 | 8.47e-01 | 6.87e-01 | 9.65e-01 | 3.80e-01 | 8.85e-01 | 7.26e-01 | 9.94e-01 | 7.26e-01 | 8.83e-01 | 6.80e-01 | 5.07e-01 | 1.79e-02 | 7.37e-01 | 7.78e-01 | 9.24e-11 | 6.99e-01 | 9.65e-01 | 8.81e-01 | 8.85e-01 | 4.98e-01 | 7.26e-01 | 7.83e-01 | 7.26e-01 | 5.67e-01 | 9.65e-01 | 7.88e-01 | 3.02e-01 | 9.05e-01 | 4.96e-01 | 9.28e-01 | 9.65e-01 | 9.73e-01 | 9.65e-01 | 9.96e-01 | 9.65e-01 | 7.26e-01 | 9.65e-01 | 3.81e-01 | 9.00e-01 | 9.08e-01 | 2.69e-19 | 5.77e-01 | 8.81e-01 | 9.36e-01 | 8.52e-03 | 1.67e-02 | 9.65e-01 | 4.75e-66 | 5.29e-01 | 8.84e-01 | 9.65e-01 | 9.76e-01 | 9.96e-01 | 9.97e-01 | 9.65e-01 | 1.14e-02 | 7.26e-01 | 5.74e-01 | 9.79e-01 | 9.78e-01 | 9.65e-01 | 9.76e-01 | 7.87e-03 | 9.65e-01 | 9.31e-01 | 9.65e-01 | 9.65e-01 | 9.65e-01 | 5.91e-01 | 9.59e-01 | 4.73e-04 | 8.81e-01 | 9.65e-01 | 9.65e-01 | 9.65e-01 | 3.19e-01 | 2.98e-01 | 4.79e-03 | 7.26e-01 | 9.65e-01 | 9.49e-01 | 7.76e-01 | 9.14e-01 | 7.82e-01 | 5.73e-01 | 9.11e-01 | 8.86e-01 | 9.94e-01 | 9.18e-01 | 9.10e-01 | 9.11e-01 | 9.81e-01 | 3.88e-01 | 9.65e-01 | 7.26e-01 | 7.26e-01 | 9.65e-01 | 4.59e-01 | 9.65e-01 | 7.91e-02 | 9.77e-01 | 7.26e-01 | 8.99e-01 | 8.88e-01 | 8.84e-01 | 8.13e-01 | 4.46e-01 | 8.83e-01 | 7.94e-02 | 9.65e-01 | 3.72e-04 | 1.48e-01 | 9.65e-01 | 9.65e-01 | 9.85e-01 | 9.65e-01 | 1.05e-01 | 1.86e-04 | 9.96e-01 | 9.65e-01 | 7.26e-01 | 9.65e-01 | 9.10e-01 | 1.19e-01 | 9.65e-01 | 6.86e-01 | 9.65e-01 | 1.31e-01 | 7.26e-01 | 8.84e-01 | 9.65e-01 | 5.82e-01 | 8.86e-01 | 9.72e-01 | 7.98e-01 | 9.65e-01 |
| 6 | 4.19e-01 | 9.93e-01 | 9.84e-01 | 9.93e-01 | 7.34e-01 | 7.91e-01 | 9.93e-01 | 9.33e-01 | 9.93e-01 | 4.96e-01 | 9.93e-01 | 7.97e-01 | 5.48e-01 | 9.68e-01 | 9.93e-01 | 9.23e-01 | 3.47e-01 | 9.24e-01 | 9.68e-01 | 6.10e-01 | 4.87e-03 | 6.37e-01 | 6.20e-01 | 7.91e-01 | 7.34e-01 | 9.68e-01 | 9.72e-01 | 3.36e-01 | 7.50e-01 | 9.68e-01 | 4.39e-01 | 9.40e-01 | 8.51e-01 | 9.97e-01 | 9.68e-01 | 9.73e-01 | 9.93e-01 | 8.88e-01 | 9.59e-01 | 9.99e-01 | 9.93e-01 | 4.01e-01 | 8.64e-01 | 4.45e-01 | 9.34e-01 | 9.52e-01 | 9.99e-01 | 9.94e-01 | 9.02e-01 | 7.92e-01 | 9.93e-01 | 3.47e-05 | 9.93e-01 | 1.07e-06 | 2.10e-01 | 0.00e+00 | 9.93e-01 | 9.93e-01 | 7.71e-01 | 9.07e-01 | 9.93e-01 | 8.78e-01 | 9.72e-01 | 5.97e-01 | 6.76e-01 | 8.38e-08 | 9.93e-01 | 9.30e-01 | 7.34e-01 | 8.12e-01 | 7.34e-01 | 5.59e-01 | 9.24e-01 | 7.34e-01 | 8.01e-01 | 9.36e-01 | 4.98e-01 | 9.93e-01 | 8.72e-01 | 9.33e-01 | 9.68e-01 | 9.33e-01 | 8.21e-01 | 9.80e-01 | 4.84e-01 | 4.24e-01 | 9.39e-01 | 6.04e-01 | 8.82e-01 | 9.68e-01 | 8.67e-01 | 8.73e-01 | 9.93e-01 | 9.66e-01 | 9.49e-01 | 9.37e-01 | 9.93e-01 | 9.93e-01 | 8.71e-01 | 9.26e-01 | 7.36e-01 | 9.93e-01 | 4.63e-01 | 4.93e-01 | 9.93e-01 | 9.75e-01 | 8.94e-01 | 6.39e-01 | 9.95e-01 | 9.73e-01 | 9.68e-01 | 9.48e-01 | 9.95e-01 | 1.63e-06 | 9.70e-01 | 9.80e-01 | 6.34e-01 | 8.09e-01 | 9.68e-01 | 6.96e-01 | 9.68e-01 | 3.16e-65 | 9.93e-01 | 2.89e-01 | 1.07e-01 | 9.63e-01 | 9.03e-48 | 9.93e-01 | 9.30e-01 | 4.65e-01 | 9.93e-01 | 5.75e-06 | 7.34e-01 | 9.79e-01 | 8.43e-01 | 7.21e-01 | 2.39e-06 | 9.93e-01 | 9.93e-01 | 8.48e-01 | 9.46e-01 | 8.31e-01 | 9.73e-01 | 7.76e-13 | 9.96e-01 | 9.73e-01 | 9.93e-01 | 7.09e-01 | 9.25e-01 | 9.93e-01 | 9.93e-01 | 1.69e-01 | 9.93e-01 | 7.94e-01 | 9.74e-01 | 9.93e-01 | 2.51e-02 | 9.27e-01 | 6.14e-01 | 8.33e-02 | 9.70e-01 | 7.41e-01 | 9.93e-01 | 8.70e-01 | 7.42e-01 | 9.93e-01 | 8.80e-01 | 9.72e-01 | 8.21e-01 | 7.34e-01 | 9.93e-01 | 9.93e-01 | 7.70e-01 | 9.93e-01 | 9.43e-01 | 6.52e-01 | 5.27e-01 | 9.93e-01 | 9.68e-01 | 9.69e-01 | 7.34e-01 | 9.93e-01 | 7.34e-01 | 8.88e-01 | 8.74e-01 | 7.60e-01 | 3.85e-02 | 8.56e-01 | 9.25e-01 | 9.72e-06 | 8.85e-01 | 9.93e-01 | 9.68e-01 | 9.60e-01 | 4.67e-01 | 7.34e-01 | 9.27e-01 | 9.93e-01 | 8.04e-01 | 9.93e-01 | 9.30e-01 | 5.76e-01 | 8.93e-01 | 8.34e-01 | 9.84e-01 | 9.93e-01 | 9.64e-01 | 9.93e-01 | 8.98e-01 | 9.93e-01 | 7.34e-01 | 9.93e-01 | 9.68e-01 | 9.75e-01 | 3.28e-06 | 2.58e-22 | 8.10e-01 | 9.68e-01 | 9.46e-01 | 4.83e-01 | 4.49e-03 | 9.93e-01 | 3.05e-52 | 7.76e-01 | 9.69e-01 | 9.93e-01 | 7.03e-01 | 9.23e-01 | 9.98e-01 | 9.93e-01 | 2.13e-01 | 9.93e-01 | 8.08e-01 | 6.26e-79 | 9.44e-01 | 9.93e-01 | 9.74e-01 | 2.58e-01 | 9.93e-01 | 9.84e-01 | 9.93e-01 | 9.93e-01 | 9.93e-01 | 8.19e-01 | 7.65e-01 | 1.37e-02 | 9.68e-01 | 9.93e-01 | 9.93e-01 | 9.93e-01 | 4.96e-01 | 5.71e-01 | 9.02e-01 | 7.34e-01 | 9.93e-01 | 9.32e-01 | 9.24e-01 | 9.86e-01 | 9.27e-01 | 9.95e-01 | 9.78e-01 | 9.70e-01 | 9.98e-01 | 7.23e-02 | 9.78e-01 | 9.78e-01 | 9.88e-01 | 6.61e-01 | 9.93e-01 | 9.93e-01 | 7.34e-01 | 9.93e-01 | 4.04e-01 | 9.93e-01 | 3.19e-01 | 9.40e-01 | 9.93e-01 | 8.60e-01 | 9.70e-01 | 9.69e-01 | 9.41e-01 | 7.12e-01 | 9.69e-01 | 9.76e-01 | 9.93e-01 | 8.50e-01 | 3.79e-01 | 9.93e-01 | 9.93e-01 | 9.80e-01 | 9.93e-01 | 7.79e-01 | 2.90e-03 | 9.93e-01 | 9.93e-01 | 7.34e-01 | 9.93e-01 | 9.04e-01 | 1.91e-01 | 9.93e-01 | 8.77e-01 | 9.93e-01 | 7.40e-01 | 7.34e-01 | 9.69e-01 | 9.93e-01 | 8.13e-01 | 9.70e-01 | 9.87e-01 | 9.34e-01 | 0.00e+00 |
| 7 | 7.03e-01 | 9.96e-01 | 9.90e-01 | 9.96e-01 | 9.07e-01 | 9.10e-01 | 9.96e-01 | 9.43e-01 | 9.96e-01 | 4.49e-01 | 9.96e-01 | 7.96e-01 | 9.51e-01 | 9.82e-01 | 9.96e-01 | 9.32e-01 | 5.30e-01 | 9.34e-01 | 1.00e+00 | 5.80e-01 | 2.12e-01 | 2.95e-01 | 1.09e-01 | 7.95e-01 | 9.07e-01 | 9.76e-01 | 9.79e-01 | 2.74e-01 | 9.07e-01 | 9.76e-01 | 7.19e-01 | 9.98e-01 | 9.66e-01 | 1.00e+00 | 9.03e-01 | 9.80e-01 | 9.96e-01 | 2.89e-01 | 6.22e-01 | 7.96e-01 | 9.96e-01 | 8.76e-01 | 9.69e-01 | 1.86e-02 | 4.66e-01 | 9.93e-01 | 1.00e+00 | 9.35e-01 | 9.82e-01 | 7.90e-01 | 9.96e-01 | 8.91e-04 | 9.96e-01 | 3.38e-06 | 7.99e-02 | 9.96e-01 | 9.96e-01 | 9.96e-01 | 7.67e-01 | 1.01e-02 | 9.96e-01 | 8.86e-01 | 9.80e-01 | 5.65e-01 | 6.57e-01 | 3.15e-06 | 9.96e-01 | 9.40e-01 | 9.96e-01 | 9.48e-01 | 9.96e-01 | 5.21e-01 | 9.34e-01 | 9.07e-01 | 8.00e-01 | 1.33e-15 | 4.52e-01 | 9.96e-01 | 8.79e-01 | 9.43e-01 | 9.76e-01 | 9.43e-01 | 8.23e-01 | 7.31e-33 | 9.94e-01 | 1.40e-01 | 5.72e-01 | 5.90e-01 | 8.91e-01 | 9.76e-01 | 9.80e-01 | 8.81e-01 | 9.96e-01 | 1.00e+00 | 9.94e-01 | 9.61e-01 | 9.96e-01 | 9.96e-01 | 8.79e-01 | 1.00e+00 | 7.26e-01 | 9.96e-01 | 4.12e-01 | 3.60e-03 | 0.00e+00 | 9.95e-01 | 8.62e-01 | 6.13e-01 | 9.99e-01 | 9.65e-01 | 9.76e-01 | 9.97e-01 | 8.36e-01 | 3.15e-74 | 9.78e-01 | 9.79e-01 | 9.46e-01 | 9.96e-01 | 9.76e-01 | 9.30e-01 | 9.76e-01 | 1.03e-63 | 9.96e-01 | 1.26e-02 | 6.15e-02 | 9.72e-01 | 9.76e-01 | 9.96e-01 | 9.39e-01 | 4.14e-01 | 9.96e-01 | 1.83e-05 | 9.07e-01 | 9.71e-01 | 2.86e-01 | 1.14e-03 | 1.64e-01 | 9.07e-01 | 9.96e-01 | 9.99e-01 | 9.96e-01 | 8.34e-01 | 9.81e-01 | 7.14e-12 | 7.04e-37 | 9.81e-01 | 9.96e-01 | 4.76e-01 | 9.35e-01 | 9.07e-01 | 9.96e-01 | 6.31e-01 | 9.96e-01 | 9.14e-01 | 9.81e-01 | 9.96e-01 | 5.01e-03 | 9.37e-01 | 4.38e-01 | 2.33e-02 | 9.78e-01 | 7.32e-01 | 9.96e-01 | 9.55e-01 | 9.93e-01 | 9.96e-01 | 9.94e-01 | 9.79e-01 | 6.24e-01 | 9.07e-01 | 9.96e-01 | 9.96e-01 | 9.96e-01 | 9.96e-01 | 9.53e-01 | 9.23e-01 | 8.76e-01 | 9.96e-01 | 6.28e-01 | 9.77e-01 | 9.07e-01 | 9.99e-01 | 9.07e-01 | 9.55e-01 | 8.81e-01 | 7.53e-01 | 5.25e-02 | 8.81e-01 | 9.35e-01 | 1.05e-40 | 8.93e-01 | 9.96e-01 | 9.76e-01 | 9.77e-01 | 7.36e-01 | 9.07e-01 | 9.37e-01 | 9.96e-01 | 8.03e-01 | 9.96e-01 | 9.39e-01 | 5.40e-01 | 9.81e-01 | 7.30e-01 | 9.89e-01 | 9.96e-01 | 9.96e-01 | 9.96e-01 | 1.00e+00 | 9.96e-01 | 9.07e-01 | 9.96e-01 | 6.29e-01 | 9.82e-01 | 2.78e-05 | 5.51e-18 | 8.11e-01 | 9.76e-01 | 9.79e-01 | 3.34e-02 | 1.91e-02 | 9.96e-01 | 6.89e-51 | 7.72e-01 | 9.77e-01 | 9.96e-01 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 3.37e-02 | 9.07e-01 | 8.08e-01 | 9.98e-01 | 9.98e-01 | 9.96e-01 | 9.97e-01 | 2.95e-02 | 9.96e-01 | 9.90e-01 | 9.96e-01 | 9.96e-01 | 9.96e-01 | 8.21e-01 | 9.93e-01 | 3.17e-03 | 9.76e-01 | 9.96e-01 | 9.96e-01 | 9.96e-01 | 5.41e-01 | 5.35e-01 | 1.48e-02 | 9.07e-01 | 9.96e-01 | 9.85e-01 | 9.34e-01 | 9.81e-01 | 9.36e-01 | 8.00e-01 | 4.23e-36 | 9.77e-01 | 9.99e-01 | 1.85e-01 | 9.84e-01 | 9.85e-01 | 9.97e-01 | 6.39e-01 | 9.96e-01 | 9.07e-01 | 9.07e-01 | 9.96e-01 | 6.99e-01 | 9.96e-01 | 7.38e-02 | 9.97e-01 | 9.07e-01 | 9.81e-01 | 9.78e-01 | 9.77e-01 | 9.50e-01 | 6.98e-01 | 9.76e-01 | 1.92e-01 | 9.96e-01 | 2.18e-02 | 3.20e-01 | 9.96e-01 | 9.96e-01 | 9.98e-01 | 9.96e-01 | 2.44e-01 | 4.45e-09 | 1.00e+00 | 9.96e-01 | 9.07e-01 | 9.96e-01 | 9.83e-01 | 2.64e-01 | 9.96e-01 | 8.85e-01 | 9.96e-01 | 2.67e-01 | 9.07e-01 | 9.77e-01 | 9.96e-01 | 8.14e-01 | 9.77e-01 | 9.94e-01 | 9.44e-01 | 0.00e+00 |
| 8 | 6.35e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.83e-01 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 7.41e-01 | 1.00e+00 | 9.39e-01 | 6.48e-01 | 5.61e-01 | 1.00e+00 | 9.86e-01 | 8.28e-01 | 9.86e-01 | 9.99e-01 | 8.30e-01 | 1.02e-01 | 5.04e-03 | 2.56e-01 | 9.99e-01 | 1.00e+00 | 9.96e-01 | 9.97e-01 | 5.82e-01 | 8.70e-01 | 9.96e-01 | 6.55e-01 | 9.90e-01 | 9.53e-01 | 1.00e+00 | 9.40e-01 | 9.97e-01 | 1.00e+00 | 6.91e-01 | 8.72e-01 | 9.91e-01 | 1.00e+00 | 9.82e-01 | 9.98e-01 | 4.49e-01 | 7.53e-01 | 9.66e-01 | 1.00e+00 | 8.85e-01 | 9.96e-01 | 9.36e-01 | 1.00e+00 | 6.33e-10 | 1.00e+00 | 9.23e-11 | 1.12e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.27e-01 | 4.66e-05 | 1.00e+00 | 9.72e-01 | 9.97e-01 | 8.21e-01 | 8.74e-01 | 1.91e-04 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 7.92e-01 | 9.86e-01 | 1.00e+00 | 9.41e-01 | 9.90e-01 | 7.43e-01 | 1.00e+00 | 9.69e-01 | 9.88e-01 | 9.96e-01 | 9.88e-01 | 9.50e-01 | 9.78e-01 | 9.95e-01 | 4.59e-01 | 7.70e-01 | 7.76e-01 | 9.73e-01 | 9.96e-01 | 9.97e-01 | 9.70e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 9.69e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.12e-01 | 2.08e-05 | 1.00e+00 | 1.00e+00 | 9.76e-01 | 8.50e-01 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 9.98e-01 | 1.14e-72 | 9.96e-01 | 6.08e-01 | 9.85e-01 | 1.00e+00 | 9.96e-01 | 8.51e-01 | 9.96e-01 | 3.64e-62 | 1.00e+00 | 7.03e-02 | 2.57e-01 | 9.95e-01 | 1.72e-45 | 1.00e+00 | 9.92e-01 | 7.13e-01 | 1.00e+00 | 2.97e-09 | 8.94e-01 | 9.97e-01 | 6.31e-01 | 3.64e-01 | 3.96e-06 | 8.94e-01 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 9.54e-01 | 9.97e-01 | 3.08e-11 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.96e-01 | 9.86e-01 | 8.94e-01 | 1.00e+00 | 9.23e-01 | 1.00e+00 | 8.07e-01 | 9.97e-01 | 1.00e+00 | 4.81e-04 | 9.87e-01 | 1.55e-01 | 2.32e-02 | 9.96e-01 | 9.11e-01 | 1.00e+00 | 8.99e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.67e-01 | 8.94e-01 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 9.91e-01 | 7.26e-05 | 9.80e-01 | 1.00e+00 | 5.44e-01 | 9.96e-01 | 8.94e-01 | 9.99e-01 | 8.94e-01 | 9.89e-01 | 9.70e-01 | 9.21e-01 | 2.63e-01 | 9.13e-01 | 9.86e-01 | 1.59e-39 | 9.74e-01 | 1.00e+00 | 9.96e-01 | 9.96e-01 | 9.34e-01 | 8.94e-01 | 9.87e-01 | 8.94e-01 | 9.42e-01 | 1.00e+00 | 9.87e-01 | 8.05e-01 | 9.94e-01 | 6.05e-01 | 9.98e-01 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.97e-01 | 7.92e-05 | 4.61e-12 | 9.45e-01 | 9.96e-01 | 7.80e-01 | 2.01e-08 | 5.71e-03 | 1.00e+00 | 7.00e-62 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.14e-02 | 8.94e-01 | 9.44e-01 | 6.45e-76 | 9.96e-01 | 1.00e+00 | 9.96e-01 | 1.51e-02 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.49e-01 | 1.00e+00 | 7.92e-03 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.59e-01 | 8.02e-01 | 3.69e-07 | 1.00e+00 | 1.00e+00 | 9.58e-02 | 9.86e-01 | 8.57e-01 | 9.87e-01 | 7.24e-01 | 9.98e-01 | 9.96e-01 | 9.61e-01 | 9.67e-12 | 9.98e-01 | 9.98e-01 | 9.97e-01 | 8.64e-01 | 1.00e+00 | 8.94e-01 | 1.00e+00 | 1.00e+00 | 9.21e-01 | 1.00e+00 | 4.90e-03 | 9.95e-01 | 8.94e-01 | 9.97e-01 | 9.96e-01 | 9.96e-01 | 9.90e-01 | 1.00e+00 | 9.96e-01 | 8.18e-01 | 1.00e+00 | 4.18e-01 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 5.48e-01 | 1.94e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 6.04e-01 | 1.00e+00 | 9.71e-01 | 1.00e+00 | 2.05e-01 | 8.94e-01 | 9.96e-01 | 1.00e+00 | 9.46e-01 | 9.96e-01 | 9.92e-01 | 9.89e-01 | 0.00e+00 |
| 9 | 7.96e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 9.17e-01 | 1.00e+00 | 9.83e-01 | 1.00e+00 | 5.88e-01 | 1.00e+00 | 8.99e-01 | 6.68e-01 | 2.63e-01 | 1.00e+00 | 9.52e-01 | 9.26e-01 | 9.79e-01 | 6.59e-01 | 7.21e-01 | 6.31e-01 | 3.66e-01 | 1.71e-01 | 9.57e-01 | 1.00e+00 | 9.95e-01 | 9.96e-01 | 3.82e-01 | 5.35e-01 | 9.95e-01 | 8.11e-01 | 9.99e-01 | 9.85e-01 | 1.00e+00 | 9.33e-01 | 9.96e-01 | 1.00e+00 | 6.03e-01 | 9.87e-01 | 8.65e-01 | 1.00e+00 | 9.66e-01 | 9.88e-01 | 3.79e-01 | 6.06e-01 | 9.43e-01 | 4.71e-01 | 9.66e-01 | 9.94e-01 | 8.95e-01 | 1.00e+00 | 6.56e-10 | 1.00e+00 | 3.38e-10 | 1.75e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.78e-01 | 1.23e-19 | 1.00e+00 | 9.55e-01 | 9.96e-01 | 7.94e-01 | 7.91e-01 | 7.35e-03 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 9.78e-01 | 1.00e+00 | 6.63e-01 | 9.79e-01 | 9.58e-01 | 9.02e-01 | 3.43e-14 | 5.91e-01 | 9.58e-01 | 9.51e-01 | 9.82e-01 | 9.95e-01 | 9.83e-01 | 9.17e-01 | 4.59e-31 | 9.98e-01 | 4.12e-01 | 6.27e-01 | 5.69e-01 | 9.57e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 8.54e-01 | 1.00e+00 | 1.00e+00 | 9.51e-01 | 7.31e-01 | 9.98e-01 | 1.00e+00 | 5.47e-01 | 3.29e-04 | 0.00e+00 | 1.00e+00 | 9.24e-01 | 9.81e-01 | 7.01e-01 | 9.97e-01 | 9.95e-01 | 1.00e+00 | 9.99e-01 | 2.11e-71 | 9.95e-01 | 7.34e-01 | 9.93e-01 | 8.98e-01 | 9.95e-01 | 7.62e-01 | 9.95e-01 | 3.59e-61 | 1.00e+00 | 9.38e-03 | 5.59e-01 | 9.93e-01 | 1.63e-44 | 1.00e+00 | 9.98e-01 | 5.49e-01 | 1.00e+00 | 9.91e-09 | 9.58e-01 | 9.84e-01 | 9.06e-01 | 4.18e-02 | 3.98e-06 | 9.58e-01 | 1.00e+00 | 9.55e-01 | 4.11e-01 | 9.24e-01 | 9.96e-01 | 2.78e-11 | 9.11e-37 | 9.96e-01 | 1.00e+00 | 9.96e-01 | 9.79e-01 | 1.15e-01 | 1.00e+00 | 6.41e-01 | 1.00e+00 | 8.75e-01 | 9.96e-01 | 9.58e-01 | 3.71e-04 | 9.80e-01 | 2.92e-02 | 3.99e-03 | 9.95e-01 | 8.52e-01 | 1.00e+00 | 4.75e-01 | 8.38e-01 | 1.00e+00 | 9.86e-01 | 9.96e-01 | 9.94e-01 | 9.58e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.87e-01 | 9.10e-01 | 8.04e-01 | 1.00e+00 | 4.15e-04 | 9.95e-01 | 9.58e-01 | 4.40e-01 | 9.58e-01 | 9.43e-01 | 9.52e-01 | 8.68e-01 | 3.70e-01 | 9.16e-01 | 9.79e-01 | 2.55e-40 | 9.59e-01 | 1.00e+00 | 9.95e-01 | 9.95e-01 | 9.95e-01 | 9.58e-01 | 9.80e-01 | 9.58e-01 | 9.04e-01 | 1.00e+00 | 9.81e-01 | 6.83e-01 | 9.91e-01 | 7.03e-05 | 9.81e-01 | 1.00e+00 | 9.71e-01 | 1.00e+00 | 7.85e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.25e-01 | 9.96e-01 | 1.31e-04 | 1.25e-11 | 9.09e-01 | 9.95e-01 | 9.61e-01 | 1.77e-02 | 4.14e-04 | 1.00e+00 | 8.28e-61 | 9.97e-01 | 9.95e-01 | 1.00e+00 | 1.42e-01 | 9.61e-01 | 1.00e+00 | 1.00e+00 | 3.24e-02 | 9.58e-01 | 9.07e-01 | 2.02e-74 | 4.87e-01 | 1.00e+00 | 9.99e-01 | 4.33e-02 | 1.00e+00 | 9.80e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.16e-01 | 9.97e-01 | 1.69e-02 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.61e-01 | 6.77e-01 | 1.17e-01 | 9.58e-01 | 1.00e+00 | 9.72e-01 | 9.79e-01 | 9.96e-01 | 9.80e-01 | 7.61e-01 | 1.52e-34 | 9.95e-01 | 1.00e+00 | 2.48e-01 | 9.97e-01 | 9.97e-01 | 9.99e-01 | 9.97e-01 | 1.00e+00 | 1.15e-01 | 9.58e-01 | 1.00e+00 | 6.95e-01 | 1.00e+00 | 8.72e-02 | 9.99e-01 | 9.58e-01 | 9.96e-01 | 9.95e-01 | 9.95e-01 | 9.86e-01 | 9.98e-01 | 9.95e-01 | 4.92e-01 | 1.00e+00 | 1.63e-01 | 9.64e-01 | 1.00e+00 | 1.00e+00 | 9.22e-01 | 1.00e+00 | 3.44e-01 | 2.01e-10 | 1.00e+00 | 1.00e+00 | 9.58e-01 | 1.00e+00 | 9.95e-01 | 7.27e-01 | 1.00e+00 | 9.54e-01 | 1.00e+00 | 2.98e-01 | 9.58e-01 | 1.00e+00 | 1.00e+00 | 9.11e-01 | 9.95e-01 | 1.00e+00 | 9.83e-01 | 0.00e+00 |
| 10 | 8.46e-01 | 9.33e-01 | 9.92e-01 | 9.33e-01 | 9.75e-01 | 9.31e-01 | 9.98e-01 | 8.53e-01 | 9.98e-01 | 6.06e-01 | 9.98e-01 | 7.66e-01 | 9.82e-01 | 9.80e-01 | 9.98e-01 | 9.03e-01 | 7.03e-01 | 9.95e-01 | 5.86e-01 | 8.79e-01 | 1.38e-01 | 2.55e-01 | 5.18e-03 | 9.56e-01 | 9.75e-01 | 9.75e-01 | 9.80e-01 | 5.00e-01 | 8.95e-01 | 9.97e-01 | 8.59e-01 | 9.98e-01 | 9.92e-01 | 1.00e+00 | 3.91e-01 | 6.38e-01 | 9.98e-01 | 6.79e-01 | 8.45e-01 | 8.99e-01 | 9.98e-01 | 6.82e-01 | 9.87e-01 | 5.99e-01 | 9.90e-01 | 9.59e-01 | 9.97e-01 | 8.83e-01 | 9.94e-01 | 6.30e-01 | 9.98e-01 | 1.87e-05 | 9.33e-01 | 1.66e-94 | 1.02e-01 | 9.33e-01 | 9.33e-01 | 9.98e-01 | 8.56e-01 | 3.80e-15 | 9.98e-01 | 8.66e-01 | 9.99e-01 | 2.43e-01 | 1.60e-01 | 5.10e-04 | 9.33e-01 | 8.41e-01 | 9.33e-01 | 9.87e-01 | 9.98e-01 | 9.07e-02 | 9.06e-01 | 9.75e-01 | 9.41e-01 | 5.40e-14 | 5.13e-01 | 9.98e-01 | 9.59e-01 | 9.95e-01 | 9.97e-01 | 9.22e-01 | 6.93e-01 | 4.18e-30 | 7.92e-01 | 1.63e-01 | 8.44e-01 | 3.58e-01 | 9.18e-01 | 9.75e-01 | 9.80e-01 | 9.42e-01 | 9.98e-01 | 7.63e-01 | 9.97e-01 | 9.88e-01 | 9.98e-01 | 9.98e-01 | 9.78e-01 | 5.23e-01 | 9.45e-01 | 9.98e-01 | 6.66e-01 | 5.67e-02 | 0.00e+00 | 9.28e-01 | 9.00e-01 | 8.57e-01 | 1.00e+00 | 9.98e-01 | 9.96e-01 | 9.28e-01 | 9.01e-01 | 5.01e-70 | 9.99e-01 | 9.87e-01 | 9.48e-01 | 8.06e-01 | 9.96e-01 | 9.84e-01 | 9.97e-01 | 5.84e-60 | 9.98e-01 | 1.04e-01 | 2.56e-01 | 9.99e-01 | 9.71e-01 | 9.33e-01 | 9.80e-01 | 5.89e-01 | 9.33e-01 | 1.65e-42 | 9.75e-01 | 2.53e-01 | 4.43e-01 | 8.73e-04 | 3.83e-01 | 9.98e-01 | 9.98e-01 | 9.69e-01 | 1.72e-01 | 9.21e-01 | 9.85e-01 | 2.58e-11 | 2.85e-37 | 9.82e-01 | 9.98e-01 | 7.98e-01 | 9.48e-01 | 2.51e-02 | 9.98e-01 | 6.09e-01 | 9.98e-01 | 2.84e-01 | 9.82e-01 | 9.98e-01 | 2.31e-10 | 9.63e-01 | 7.07e-01 | 1.48e-01 | 9.99e-01 | 7.71e-01 | 9.33e-01 | 9.84e-01 | 7.54e-01 | 9.33e-01 | 9.67e-01 | 9.80e-01 | 7.78e-01 | 9.75e-01 | 9.33e-01 | 9.98e-01 | 9.15e-01 | 9.98e-01 | 8.45e-01 | 7.87e-01 | 8.53e-01 | 9.33e-01 | 1.60e-06 | 9.99e-01 | 9.75e-01 | 9.98e-01 | 9.98e-01 | 9.87e-01 | 7.87e-01 | 9.31e-01 | 2.73e-02 | 9.45e-01 | 9.95e-01 | 8.21e-38 | 9.89e-01 | 9.98e-01 | 5.82e-01 | 9.89e-01 | 8.22e-01 | 9.75e-01 | 9.94e-01 | 9.98e-01 | 5.93e-01 | 9.98e-01 | 9.98e-01 | 5.72e-02 | 9.77e-01 | 1.80e-08 | 9.92e-01 | 9.33e-01 | 9.99e-01 | 9.33e-01 | 5.23e-01 | 9.33e-01 | 9.75e-01 | 9.98e-01 | 7.82e-01 | 9.83e-01 | 7.48e-05 | 3.25e-17 | 9.13e-01 | 9.75e-01 | 9.87e-01 | 9.64e-02 | 9.04e-02 | 9.33e-01 | 7.76e-60 | 7.56e-01 | 9.76e-01 | 9.98e-01 | 1.52e-02 | 7.85e-01 | 9.91e-01 | 9.98e-01 | 1.16e-01 | 9.75e-01 | 4.72e-01 | 9.19e-01 | 2.35e-01 | 9.98e-01 | 9.88e-01 | 1.84e-01 | 9.98e-01 | 9.99e-01 | 9.98e-01 | 9.98e-01 | 9.98e-01 | 9.74e-01 | 7.15e-01 | 1.75e-02 | 9.75e-01 | 9.33e-01 | 9.98e-01 | 9.98e-01 | 8.37e-01 | 7.24e-01 | 7.23e-02 | 9.75e-01 | 9.98e-01 | 9.92e-01 | 9.95e-01 | 9.94e-01 | 9.95e-01 | 5.38e-01 | 2.37e-33 | 6.02e-01 | 9.42e-01 | 3.99e-01 | 9.86e-01 | 7.09e-01 | 1.00e+00 | 6.55e-01 | 9.98e-01 | 2.51e-02 | 9.75e-01 | 9.33e-01 | 8.03e-01 | 9.98e-01 | 1.21e-01 | 9.99e-01 | 9.98e-01 | 9.99e-01 | 9.99e-01 | 9.76e-01 | 9.95e-01 | 8.77e-01 | 9.94e-01 | 4.50e-01 | 9.33e-01 | 8.54e-01 | 6.78e-01 | 9.98e-01 | 9.98e-01 | 9.23e-01 | 9.98e-01 | 9.36e-01 | 4.72e-21 | 9.72e-01 | 9.98e-01 | 9.75e-01 | 9.98e-01 | 9.95e-01 | 4.41e-03 | 9.33e-01 | 8.75e-01 | 9.98e-01 | 5.30e-01 | 9.98e-01 | 9.76e-01 | 9.98e-01 | 2.31e-02 | 9.92e-01 | 9.88e-01 | 9.67e-01 | 0.00e+00 |
| 11 | 8.83e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 1.00e+00 | 9.87e-01 | 1.00e+00 | 5.16e-01 | 1.00e+00 | 8.99e-01 | 8.67e-01 | 2.60e-01 | 1.00e+00 | 1.00e+00 | 9.31e-01 | 9.84e-01 | 8.29e-01 | 6.80e-01 | 4.63e-01 | 1.05e-03 | 1.71e-01 | 9.92e-01 | 1.00e+00 | 9.97e-01 | 9.98e-01 | 2.81e-01 | 8.56e-01 | 9.97e-01 | 8.94e-01 | 9.93e-01 | 9.47e-01 | 1.00e+00 | 9.12e-01 | 9.98e-01 | 1.00e+00 | 6.39e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.55e-01 | 9.58e-01 | 7.10e-01 | 8.89e-01 | 9.69e-01 | 5.86e-01 | 8.81e-01 | 9.97e-01 | 8.94e-01 | 1.00e+00 | 1.79e-06 | 1.00e+00 | 9.13e-01 | 9.42e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.74e-01 | 4.45e-04 | 1.00e+00 | 9.61e-01 | 9.98e-01 | 8.36e-01 | 7.67e-01 | 8.79e-07 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 6.08e-01 | 9.84e-01 | 9.04e-01 | 9.02e-01 | 2.36e-13 | 8.78e-01 | 9.85e-01 | 9.57e-01 | 9.87e-01 | 9.97e-01 | 9.87e-01 | 9.20e-01 | 1.27e-29 | 9.81e-01 | 8.72e-01 | 8.88e-01 | 2.54e-01 | 9.63e-01 | 9.97e-01 | 9.55e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 9.86e-01 | 1.00e+00 | 1.00e+00 | 9.57e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 4.67e-01 | 1.23e-04 | 0.00e+00 | 1.00e+00 | 9.97e-01 | 9.70e-01 | 7.92e-01 | 9.95e-01 | 9.97e-01 | 9.90e-01 | 9.97e-01 | 8.77e-69 | 9.97e-01 | 6.13e-01 | 9.92e-01 | 9.97e-01 | 9.97e-01 | 7.95e-01 | 9.97e-01 | 1.00e-58 | 1.00e+00 | 2.31e-01 | 3.54e-01 | 9.96e-01 | 2.59e-42 | 1.00e+00 | 9.99e-01 | 4.69e-01 | 1.00e+00 | 1.52e-41 | 9.85e-01 | 9.71e-01 | 9.91e-01 | 4.05e-03 | 1.43e-04 | 9.85e-01 | 1.00e+00 | 9.75e-01 | 9.99e-01 | 9.28e-01 | 9.98e-01 | 8.20e-10 | 1.53e-33 | 9.98e-01 | 1.00e+00 | 9.94e-01 | 9.84e-01 | 9.85e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.09e-01 | 9.98e-01 | 1.00e+00 | 7.74e-11 | 9.85e-01 | 1.27e-01 | 3.70e-02 | 9.97e-01 | 8.42e-01 | 1.00e+00 | 7.22e-14 | 9.95e-01 | 1.00e+00 | 9.98e-01 | 9.98e-01 | 1.00e+00 | 9.85e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.91e-01 | 8.57e-01 | 8.96e-01 | 1.00e+00 | 8.27e-01 | 9.97e-01 | 9.85e-01 | 9.32e-01 | 9.85e-01 | 9.99e-01 | 9.58e-01 | 8.62e-01 | 7.87e-01 | 9.98e-01 | 9.84e-01 | 1.95e-76 | 9.65e-01 | 1.00e+00 | 9.97e-01 | 9.91e-01 | 9.73e-01 | 0.00e+00 | 9.85e-01 | 9.85e-01 | 9.05e-01 | 1.00e+00 | 9.68e-01 | 6.32e-01 | 9.97e-01 | 8.52e-01 | 9.94e-01 | 1.00e+00 | 9.77e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.42e-01 | 9.98e-01 | 4.43e-04 | 1.85e-14 | 9.10e-01 | 9.97e-01 | 8.78e-01 | 7.44e-05 | 2.49e-06 | 1.00e+00 | 1.96e-44 | 9.96e-01 | 9.97e-01 | 1.00e+00 | 9.94e-01 | 9.89e-01 | 9.95e-01 | 1.00e+00 | 5.70e-02 | 9.04e-01 | 9.09e-01 | 1.16e-71 | 9.20e-01 | 9.04e-01 | 1.00e+00 | 1.74e-02 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.18e-01 | 9.95e-01 | 1.16e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 6.25e-01 | 3.41e-06 | 9.85e-01 | 1.00e+00 | 8.47e-01 | 9.84e-01 | 9.54e-01 | 9.85e-01 | 9.24e-01 | 9.23e-33 | 9.97e-01 | 7.96e-01 | 2.69e-01 | 9.64e-01 | 9.99e-01 | 9.31e-01 | 9.58e-01 | 1.00e+00 | 9.85e-01 | 9.85e-01 | 1.00e+00 | 7.14e-01 | 1.00e+00 | 7.11e-01 | 9.89e-01 | 9.85e-01 | 9.52e-01 | 9.97e-01 | 9.97e-01 | 9.90e-01 | 9.95e-01 | 9.97e-01 | 8.55e-01 | 1.00e+00 | 5.38e-01 | 9.22e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.40e-01 | 2.78e-04 | 9.75e-01 | 1.00e+00 | 9.85e-01 | 1.00e+00 | 9.97e-01 | 5.47e-01 | 1.00e+00 | 9.60e-01 | 1.00e+00 | 6.56e-10 | 9.85e-01 | 1.00e+00 | 1.00e+00 | 9.13e-01 | 9.97e-01 | 9.93e-01 | 9.97e-01 | 0.00e+00 |
| 12 | 9.11e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.91e-01 | 9.54e-01 | 9.91e-01 | 1.00e+00 | 9.91e-01 | 9.17e-01 | 1.00e+00 | 8.32e-01 | 9.98e-01 | 9.37e-01 | 1.00e+00 | 9.95e-01 | 6.87e-01 | 9.74e-01 | 8.74e-01 | 9.30e-01 | 5.97e-02 | 9.15e-04 | 4.18e-01 | 9.99e-01 | 9.91e-01 | 1.00e+00 | 9.96e-01 | 8.01e-01 | 7.74e-01 | 9.95e-01 | 9.20e-01 | 9.88e-01 | 2.07e-02 | 9.28e-01 | 9.73e-01 | 9.97e-01 | 9.91e-01 | 2.98e-01 | 4.80e-01 | 5.00e-01 | 1.00e+00 | 8.36e-01 | 9.32e-01 | 2.06e-01 | 8.99e-01 | 9.93e-01 | 1.00e+00 | 9.79e-01 | 9.98e-01 | 8.24e-01 | 1.00e+00 | 4.55e-06 | 1.00e+00 | 1.09e-08 | 1.13e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 1.14e-17 | 9.91e-01 | 9.95e-01 | 9.97e-01 | 8.59e-01 | 6.35e-01 | 1.99e-11 | 1.00e+00 | 9.77e-01 | 9.91e-01 | 9.96e-01 | 9.91e-01 | 4.32e-01 | 9.74e-01 | 8.63e-01 | 8.38e-01 | 1.16e-01 | 5.55e-01 | 1.00e+00 | 9.27e-01 | 9.79e-01 | 9.95e-01 | 9.80e-01 | 9.93e-01 | 8.39e-01 | 9.73e-01 | 2.00e-01 | 7.89e-01 | 3.37e-01 | 9.97e-01 | 1.00e+00 | 1.79e-07 | 9.91e-01 | 1.00e+00 | 9.97e-01 | 9.99e-01 | 9.67e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 8.70e-01 | 9.56e-01 | 1.00e+00 | 7.82e-01 | 3.44e-02 | 8.63e-01 | 9.98e-01 | 9.91e-01 | 5.70e-01 | 9.84e-01 | 9.48e-01 | 9.95e-01 | 1.67e-02 | 9.98e-01 | 2.16e-67 | 9.96e-01 | 9.79e-01 | 4.22e-01 | 8.85e-01 | 9.95e-01 | 8.93e-01 | 9.95e-01 | 1.51e-57 | 1.00e+00 | 6.45e-01 | 2.22e-01 | 9.94e-01 | 1.73e-41 | 1.00e+00 | 1.00e+00 | 2.83e-01 | 1.00e+00 | 2.14e-07 | 8.63e-01 | 9.70e-01 | 4.09e-01 | 1.93e-02 | 2.32e-06 | 9.91e-01 | 1.00e+00 | 9.98e-01 | 5.16e-01 | 9.87e-01 | 1.00e+00 | 9.85e-10 | 8.25e-01 | 9.99e-01 | 9.91e-01 | 4.66e-01 | 1.00e+00 | 1.74e-01 | 9.91e-01 | 7.99e-01 | 1.00e+00 | 9.81e-01 | 9.97e-01 | 9.91e-01 | 2.79e-03 | 9.76e-01 | 5.17e-01 | 3.59e-04 | 9.96e-01 | 7.45e-01 | 1.00e+00 | 1.82e-13 | 9.67e-01 | 1.00e+00 | 9.96e-01 | 9.99e-01 | 9.92e-01 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 8.66e-01 | 1.00e+00 | 9.85e-01 | 2.50e-01 | 9.10e-01 | 1.00e+00 | 3.73e-04 | 9.96e-01 | 9.91e-01 | 1.00e+00 | 9.91e-01 | 8.51e-01 | 9.30e-01 | 7.75e-01 | 2.86e-01 | 8.18e-01 | 9.74e-01 | 3.24e-75 | 9.99e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 8.90e-01 | 1.08e-02 | 1.00e+00 | 1.00e+00 | 8.41e-01 | 1.00e+00 | 9.56e-01 | 4.60e-01 | 9.98e-01 | 3.49e-05 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 8.96e-01 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 2.02e-01 | 9.99e-01 | 6.61e-04 | 2.92e-21 | 9.96e-01 | 9.95e-01 | 7.65e-01 | 2.01e-06 | 8.01e-05 | 1.00e+00 | 2.34e-58 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.80e-01 | 9.51e-01 | 1.00e+00 | 1.00e+00 | 6.51e-02 | 1.08e-02 | 8.48e-01 | 2.46e-70 | 5.99e-01 | 1.00e+00 | 1.00e+00 | 1.15e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 9.97e-01 | 9.87e-01 | 8.73e-03 | 9.95e-01 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 3.53e-01 | 7.67e-01 | 4.19e-10 | 9.91e-01 | 1.00e+00 | 5.78e-01 | 9.74e-01 | 9.99e-01 | 9.75e-01 | 8.62e-01 | 9.40e-01 | 9.96e-01 | 9.99e-01 | 3.80e-02 | 9.29e-01 | 9.98e-01 | 1.18e-01 | 9.99e-01 | 1.00e+00 | 1.74e-01 | 9.91e-01 | 1.00e+00 | 7.52e-01 | 1.00e+00 | 1.98e-01 | 1.63e-01 | 9.91e-01 | 1.33e-07 | 9.96e-01 | 9.96e-01 | 9.84e-01 | 9.44e-01 | 1.00e+00 | 9.08e-01 | 1.00e+00 | 9.77e-03 | 9.75e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 4.46e-01 | 3.44e-03 | 9.34e-01 | 9.91e-01 | 9.91e-01 | 1.00e+00 | 9.98e-01 | 1.57e-01 | 1.00e+00 | 9.33e-01 | 1.00e+00 | 5.14e-02 | 9.91e-01 | 9.96e-01 | 1.00e+00 | 8.55e-01 | 1.00e+00 | 8.24e-01 | 9.80e-01 | 0.00e+00 |
| 13 | 5.18e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.10e-01 | 9.87e-01 | 1.00e+00 | 9.96e-01 | 9.97e-01 | 9.18e-01 | 9.97e-01 | 9.99e-01 | 1.57e-01 | 9.91e-01 | 9.97e-01 | 1.00e+00 | 8.86e-01 | 1.00e+00 | 9.28e-01 | 9.97e-01 | 2.16e-01 | 2.16e-05 | 2.16e-01 | 9.99e-01 | 1.00e+00 | 9.26e-01 | 9.99e-01 | 9.62e-01 | 2.08e-01 | 1.00e+00 | 1.98e-06 | 1.00e+00 | 1.29e-07 | 9.98e-01 | 9.66e-01 | 1.00e+00 | 1.00e+00 | 8.20e-01 | 9.18e-01 | 1.00e+00 | 1.00e+00 | 7.28e-01 | 1.00e+00 | 5.44e-01 | 9.83e-01 | 9.94e-01 | 1.00e+00 | 9.99e-01 | 9.69e-01 | 9.45e-01 | 9.97e-01 | 7.21e-07 | 1.00e+00 | 4.11e-89 | 5.34e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 8.89e-02 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 6.84e-01 | 9.99e-01 | 3.07e-11 | 1.00e+00 | 9.96e-01 | 9.10e-01 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 9.97e-01 | 1.00e+00 | 9.99e-01 | 1.66e-12 | 5.75e-01 | 1.00e+00 | 9.84e-01 | 9.96e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 8.00e-28 | 3.36e-02 | 8.03e-01 | 9.35e-01 | 8.59e-01 | 9.36e-01 | 9.99e-01 | 1.00e+00 | 9.83e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.48e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.90e-01 | 1.13e-02 | 0.00e+00 | 1.00e+00 | 9.77e-01 | 9.98e-01 | 9.99e-01 | 9.98e-01 | 9.99e-01 | 1.00e+00 | 6.38e-01 | 3.05e-66 | 9.35e-01 | 9.42e-01 | 9.63e-01 | 9.99e-01 | 9.99e-01 | 5.35e-01 | 9.99e-01 | 5.46e-56 | 1.00e+00 | 8.80e-01 | 6.81e-01 | 1.00e+00 | 1.34e-40 | 9.97e-01 | 9.98e-01 | 8.67e-01 | 9.97e-01 | 3.03e-39 | 1.00e+00 | 9.97e-01 | 9.38e-01 | 1.79e-05 | 2.95e-06 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.37e-09 | 6.29e-31 | 9.47e-01 | 1.00e+00 | 8.39e-01 | 1.00e+00 | 9.97e-01 | 9.97e-01 | 7.57e-16 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 1.00e+00 | 8.69e-09 | 1.00e+00 | 2.31e-01 | 1.12e-03 | 9.35e-01 | 9.14e-01 | 1.00e+00 | 7.61e-01 | 9.99e-01 | 9.97e-01 | 9.83e-01 | 9.42e-01 | 8.11e-01 | 9.10e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.13e-01 | 1.13e-02 | 5.03e-01 | 9.97e-01 | 9.26e-01 | 9.99e-01 | 9.10e-01 | 9.99e-01 | 9.10e-01 | 9.94e-01 | 9.98e-01 | 9.94e-01 | 3.11e-01 | 9.70e-01 | 9.94e-01 | 2.71e-35 | 1.00e+00 | 9.97e-01 | 9.26e-01 | 1.00e+00 | 9.10e-01 | 5.29e-04 | 9.99e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.60e-01 | 9.59e-01 | 8.02e-01 | 1.00e+00 | 9.97e-01 | 9.81e-01 | 1.00e+00 | 8.91e-01 | 1.00e+00 | 9.10e-01 | 1.00e+00 | 1.67e-01 | 1.00e+00 | 6.27e-05 | 1.70e-19 | 9.97e-01 | 1.00e+00 | 5.77e-01 | 8.44e-04 | 3.88e-02 | 9.97e-01 | 6.45e-58 | 1.76e-01 | 9.31e-01 | 9.97e-01 | 9.68e-01 | 9.98e-01 | 9.99e-01 | 9.97e-01 | 5.34e-01 | 5.29e-04 | 1.00e+00 | 1.83e-69 | 9.73e-01 | 1.00e+00 | 9.95e-01 | 4.86e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.97e-01 | 8.13e-01 | 9.99e-01 | 9.45e-06 | 9.99e-01 | 1.00e+00 | 9.97e-01 | 9.97e-01 | 6.08e-01 | 9.81e-01 | 1.15e-18 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 7.16e-01 | 9.98e-01 | 9.95e-01 | 9.98e-01 | 1.17e-30 | 9.99e-01 | 9.99e-01 | 3.67e-02 | 1.00e+00 | 1.00e+00 | 7.53e-02 | 9.98e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 3.86e-01 | 9.97e-01 | 1.98e-01 | 5.10e-02 | 9.97e-01 | 1.00e+00 | 9.35e-01 | 9.31e-01 | 9.97e-01 | 9.02e-01 | 9.99e-01 | 9.93e-01 | 1.00e+00 | 6.20e-07 | 6.28e-01 | 1.00e+00 | 9.97e-01 | 9.82e-01 | 1.00e+00 | 9.71e-01 | 3.49e-19 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.71e-01 | 8.46e-01 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 1.79e-01 | 9.10e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 7.61e-01 | 9.99e-01 | 0.00e+00 |
| 14 | 2.25e-01 | 4.83e-01 | 1.00e+00 | 4.83e-01 | 1.00e+00 | 9.38e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 4.87e-03 | 1.00e+00 | 8.58e-01 | 7.44e-01 | 9.47e-01 | 1.00e+00 | 9.93e-01 | 8.33e-01 | 9.97e-01 | 9.53e-01 | 9.39e-01 | 2.51e-02 | 7.26e-07 | 1.09e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.80e-02 | 8.95e-01 | 9.51e-01 | 1.13e-01 | 9.98e-01 | 7.21e-01 | 9.91e-01 | 9.49e-01 | 9.58e-01 | 1.00e+00 | 6.49e-03 | 9.68e-01 | 3.56e-01 | 1.00e+00 | 8.74e-01 | 8.32e-01 | 5.68e-01 | 9.72e-01 | 6.94e-01 | 6.89e-01 | 8.43e-01 | 8.03e-01 | 9.61e-01 | 1.00e+00 | 3.32e-07 | 1.00e+00 | 1.49e-87 | 3.14e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 5.25e-01 | 3.87e-17 | 1.00e+00 | 5.42e-01 | 9.98e-01 | 3.50e-01 | 9.47e-04 | 5.65e-07 | 4.83e-01 | 9.31e-01 | 6.32e-01 | 1.00e+00 | 1.00e+00 | 4.69e-06 | 9.97e-01 | 8.24e-01 | 4.61e-01 | 6.11e-02 | 7.48e-01 | 1.37e-01 | 5.29e-01 | 9.98e-01 | 9.67e-01 | 9.98e-01 | 9.74e-01 | 3.28e-01 | 9.87e-01 | 9.40e-01 | 8.79e-01 | 1.47e-02 | 9.79e-01 | 1.00e+00 | 7.51e-01 | 9.87e-01 | 1.00e+00 | 1.95e-01 | 9.84e-01 | 9.61e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 7.50e-01 | 7.49e-01 | 1.00e+00 | 7.71e-01 | 6.06e-03 | 1.37e-01 | 1.00e+00 | 9.25e-01 | 9.93e-01 | 9.66e-01 | 9.91e-01 | 9.35e-01 | 7.45e-01 | 4.09e-01 | 7.66e-65 | 9.94e-01 | 9.75e-01 | 9.15e-01 | 9.76e-01 | 1.00e+00 | 1.78e-01 | 9.57e-01 | 6.40e-55 | 1.00e+00 | 3.21e-01 | 2.44e-01 | 1.00e+00 | 1.22e-39 | 4.83e-01 | 9.57e-01 | 7.81e-01 | 4.83e-01 | 5.88e-42 | 6.32e-01 | 9.83e-01 | 9.89e-01 | 7.97e-05 | 4.44e-09 | 1.00e+00 | 1.00e+00 | 8.61e-01 | 7.01e-01 | 9.77e-01 | 1.00e+00 | 4.89e-09 | 1.60e-01 | 1.00e+00 | 1.00e+00 | 3.28e-01 | 9.91e-01 | 3.07e-01 | 1.00e+00 | 8.89e-02 | 1.00e+00 | 9.78e-01 | 1.00e+00 | 1.00e+00 | 2.26e-10 | 9.97e-01 | 3.20e-01 | 7.30e-02 | 9.94e-01 | 8.09e-01 | 1.00e+00 | 6.81e-13 | 9.69e-01 | 8.24e-01 | 9.95e-01 | 1.00e+00 | 9.74e-01 | 6.32e-01 | 8.24e-01 | 1.00e+00 | 9.90e-01 | 1.00e+00 | 1.23e-01 | 6.05e-01 | 9.52e-01 | 8.24e-01 | 1.43e-03 | 1.00e+00 | 6.32e-01 | 9.30e-01 | 6.32e-01 | 6.76e-01 | 9.92e-01 | 9.81e-01 | 7.89e-01 | 6.45e-01 | 9.94e-01 | 4.34e-74 | 9.91e-01 | 1.00e+00 | 1.07e-02 | 9.91e-01 | 9.02e-01 | 8.24e-01 | 9.97e-01 | 8.24e-01 | 6.17e-01 | 1.00e+00 | 7.99e-01 | 9.10e-02 | 7.09e-01 | 1.84e-05 | 1.00e+00 | 4.83e-01 | 8.02e-01 | 4.83e-01 | 9.40e-01 | 1.00e+00 | 1.00e+00 | 8.24e-01 | 9.92e-01 | 1.00e+00 | 1.06e-03 | 2.28e-22 | 9.92e-01 | 9.92e-01 | 6.43e-01 | 8.03e-03 | 6.48e-02 | 8.24e-01 | 1.05e-55 | 9.64e-01 | 1.00e+00 | 1.00e+00 | 2.78e-01 | 9.89e-01 | 9.93e-01 | 1.00e+00 | 1.36e-01 | 8.24e-01 | 1.26e-01 | 1.77e-68 | 7.73e-01 | 1.00e+00 | 9.88e-01 | 5.17e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.43e-01 | 9.86e-01 | 2.11e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.11e-01 | 9.52e-01 | 1.10e-05 | 1.00e+00 | 1.00e+00 | 9.51e-01 | 9.93e-01 | 9.95e-01 | 9.97e-01 | 7.14e-01 | 2.89e-01 | 9.92e-01 | 9.95e-01 | 6.47e-02 | 7.97e-01 | 1.00e+00 | 9.81e-01 | 9.66e-01 | 1.00e+00 | 3.07e-01 | 1.00e+00 | 1.00e+00 | 7.85e-01 | 1.00e+00 | 1.26e-01 | 4.96e-01 | 1.00e+00 | 7.26e-01 | 9.94e-01 | 1.00e+00 | 9.99e-01 | 7.36e-01 | 9.06e-01 | 9.85e-01 | 4.83e-01 | 5.47e-01 | 5.13e-01 | 1.00e+00 | 1.00e+00 | 8.92e-01 | 1.00e+00 | 6.83e-01 | 2.86e-10 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.51e-06 | 1.00e+00 | 9.57e-01 | 1.00e+00 | 1.80e-01 | 6.32e-01 | 1.00e+00 | 1.00e+00 | 5.06e-06 | 8.70e-01 | 9.95e-01 | 8.81e-01 | 0.00e+00 |
| 15 | 9.66e-01 | 1.00e+00 | 1.00e+00 | 8.76e-01 | 9.98e-01 | 9.83e-01 | 1.00e+00 | 6.00e-01 | 1.00e+00 | 1.29e-01 | 1.00e+00 | 4.10e-01 | 5.43e-02 | 9.33e-01 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 9.98e-01 | 9.96e-01 | 8.00e-01 | 2.57e-01 | 1.61e-04 | 2.04e-07 | 9.96e-01 | 9.98e-01 | 9.98e-01 | 9.99e-01 | 2.41e-01 | 9.73e-01 | 9.95e-01 | 5.23e-01 | 1.00e+00 | 9.53e-01 | 1.00e+00 | 9.28e-01 | 9.45e-05 | 1.00e+00 | 9.19e-01 | 9.78e-01 | 9.59e-01 | 1.00e+00 | 8.63e-01 | 9.98e-01 | 7.40e-01 | 8.70e-01 | 9.97e-01 | 1.00e+00 | 9.61e-01 | 9.69e-01 | 9.89e-01 | 1.00e+00 | 6.65e-08 | 8.76e-01 | 2.06e-07 | 8.55e-05 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.22e-01 | 9.44e-06 | 1.00e+00 | 5.49e-19 | 1.00e+00 | 2.77e-22 | 1.32e-03 | 1.53e-14 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 5.16e-02 | 9.83e-01 | 9.98e-01 | 9.75e-01 | 6.59e-12 | 2.81e-02 | 1.00e+00 | 9.92e-01 | 9.99e-01 | 9.97e-01 | 1.00e+00 | 8.75e-01 | 1.78e-26 | 6.34e-01 | 6.05e-01 | 9.64e-01 | 2.35e-07 | 1.74e-02 | 9.98e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 6.98e-01 | 1.00e+00 | 9.29e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.77e-01 | 9.84e-01 | 1.00e+00 | 6.59e-01 | 5.69e-04 | 0.00e+00 | 1.00e+00 | 9.81e-01 | 9.90e-01 | 1.00e+00 | 9.97e-01 | 9.93e-01 | 9.86e-01 | 3.38e-02 | 1.71e-63 | 9.99e-01 | 9.73e-01 | 9.72e-01 | 2.39e-01 | 9.95e-01 | 6.86e-01 | 9.87e-01 | 3.18e-54 | 1.00e+00 | 4.66e-01 | 5.48e-02 | 9.99e-01 | 1.46e-38 | 8.76e-01 | 9.52e-01 | 4.36e-01 | 8.76e-01 | 9.50e-56 | 9.98e-01 | 9.77e-01 | 7.91e-01 | 1.26e-05 | 4.14e-04 | 8.10e-01 | 1.00e+00 | 9.93e-01 | 9.66e-01 | 9.83e-01 | 9.99e-01 | 1.34e-08 | 5.22e-31 | 9.99e-01 | 1.00e+00 | 9.87e-01 | 8.44e-01 | 8.10e-01 | 1.00e+00 | 7.76e-01 | 1.00e+00 | 9.56e-01 | 9.99e-01 | 1.00e+00 | 1.95e-08 | 8.34e-01 | 5.29e-01 | 1.09e-02 | 9.99e-01 | 8.88e-10 | 1.00e+00 | 4.47e-01 | 9.37e-01 | 1.69e-07 | 9.90e-01 | 9.99e-01 | 2.61e-09 | 9.98e-01 | 1.69e-07 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 5.93e-01 | 2.98e-01 | 8.09e-01 | 1.69e-07 | 1.31e-01 | 9.99e-01 | 9.20e-01 | 9.91e-01 | 9.20e-01 | 9.97e-01 | 3.16e-01 | 9.01e-01 | 5.95e-01 | 8.12e-01 | 9.98e-01 | 5.37e-34 | 9.53e-01 | 1.00e+00 | 2.30e-01 | 9.93e-01 | 2.11e-02 | 0.00e+00 | 9.84e-01 | 8.10e-01 | 1.44e-01 | 1.00e+00 | 1.00e+00 | 5.54e-04 | 7.62e-47 | 6.53e-02 | 1.00e+00 | 8.76e-01 | 9.76e-01 | 8.76e-01 | 9.87e-01 | 8.76e-01 | 9.98e-01 | 9.98e-01 | 9.22e-01 | 9.99e-01 | 1.89e-03 | 1.98e-21 | 9.71e-01 | 1.00e+00 | 9.03e-01 | 2.58e-02 | 2.52e-05 | 1.69e-07 | 3.35e-45 | 9.85e-01 | 9.98e-01 | 1.00e+00 | 6.87e-01 | 9.97e-01 | 9.97e-01 | 1.00e+00 | 6.86e-02 | 9.98e-01 | 6.82e-02 | 2.23e-67 | 9.69e-01 | 9.98e-01 | 9.41e-01 | 1.05e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 9.92e-01 | 3.31e-03 | 9.98e-01 | 8.76e-01 | 1.00e+00 | 1.00e+00 | 8.40e-01 | 1.11e-01 | 4.64e-06 | 9.98e-01 | 1.00e+00 | 9.43e-01 | 9.98e-01 | 7.97e-01 | 9.98e-01 | 9.69e-01 | 3.74e-29 | 2.54e-01 | 9.47e-01 | 5.55e-01 | 1.00e+00 | 4.11e-01 | 9.95e-01 | 9.85e-01 | 1.00e+00 | 8.10e-01 | 9.98e-01 | 8.76e-01 | 8.11e-01 | 1.00e+00 | 6.12e-01 | 8.19e-01 | 8.10e-01 | 9.96e-01 | 9.99e-01 | 9.98e-01 | 9.99e-01 | 5.11e-01 | 9.90e-01 | 8.19e-01 | 8.76e-01 | 4.27e-02 | 8.16e-01 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 1.00e+00 | 1.03e-03 | 4.50e-08 | 9.74e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 9.63e-03 | 8.76e-01 | 1.07e-01 | 9.98e-01 | 6.13e-09 | 9.20e-01 | 9.98e-01 | 1.00e+00 | 6.08e-06 | 9.85e-01 | 9.81e-01 | 1.63e-07 | 0.00e+00 |
| 16 | 9.50e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.76e-01 | 6.67e-02 | 9.97e-01 | 6.25e-01 | 2.79e-02 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.97e-01 | 9.89e-01 | 7.65e-01 | 4.77e-01 | 1.49e-05 | 5.79e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.71e-01 | 8.05e-01 | 1.00e+00 | 9.57e-01 | 1.00e+00 | 9.10e-01 | 1.00e+00 | 8.91e-01 | 3.20e-01 | 1.00e+00 | 4.63e-01 | 9.97e-01 | 9.67e-01 | 1.00e+00 | 7.20e-01 | 9.96e-01 | 2.80e-01 | 9.28e-01 | 7.54e-01 | 6.71e-01 | 9.88e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.02e-06 | 9.87e-01 | 5.20e-07 | 3.04e-03 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.41e-01 | 1.96e-04 | 1.00e+00 | 7.19e-04 | 1.00e+00 | 1.34e-16 | 8.52e-01 | 1.73e-06 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.96e-01 | 9.87e-01 | 6.85e-01 | 9.99e-03 | 9.87e-01 | 9.59e-01 | 4.75e-13 | 2.24e-04 | 9.98e-01 | 9.88e-01 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 9.70e-01 | 5.42e-26 | 8.63e-01 | 9.17e-01 | 2.26e-05 | 2.98e-01 | 1.34e-02 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.23e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 9.99e-01 | 1.00e+00 | 5.15e-01 | 8.30e-04 | 0.00e+00 | 9.48e-01 | 9.95e-01 | 9.83e-01 | 9.79e-01 | 1.49e-06 | 1.00e+00 | 9.99e-01 | 5.68e-01 | 1.58e-62 | 1.00e+00 | 6.35e-01 | 1.10e-09 | 8.87e-01 | 1.00e+00 | 7.88e-01 | 1.00e+00 | 3.96e-53 | 1.00e+00 | 1.62e-01 | 7.16e-01 | 1.00e+00 | 4.51e-39 | 1.00e+00 | 1.55e-19 | 6.97e-01 | 1.00e+00 | 2.02e-36 | 9.87e-01 | 9.80e-01 | 9.97e-01 | 9.88e-07 | 1.33e-04 | 9.98e-01 | 1.00e+00 | 9.87e-01 | 1.00e+00 | 9.85e-01 | 1.00e+00 | 4.81e-08 | 4.55e-29 | 9.90e-01 | 1.00e+00 | 9.96e-01 | 9.97e-01 | 8.63e-01 | 1.00e+00 | 9.89e-01 | 1.00e+00 | 1.19e-07 | 1.00e+00 | 9.98e-01 | 6.69e-09 | 1.00e+00 | 1.74e-01 | 1.03e-02 | 1.00e+00 | 2.21e-05 | 1.00e+00 | 4.42e-12 | 9.85e-01 | 6.67e-02 | 9.97e-01 | 9.80e-01 | 9.96e-01 | 9.98e-01 | 6.67e-02 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.96e-01 | 9.54e-01 | 9.49e-01 | 6.67e-02 | 1.78e-01 | 1.00e+00 | 9.98e-01 | 9.74e-01 | 9.98e-01 | 1.00e+00 | 9.57e-01 | 9.75e-01 | 6.55e-01 | 1.43e-03 | 1.51e-03 | 1.84e-70 | 7.57e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 7.56e-02 | 0.00e+00 | 9.98e-01 | 0.00e+00 | 7.74e-01 | 1.00e+00 | 9.96e-01 | 9.87e-04 | 9.99e-01 | 9.30e-02 | 9.94e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.68e-01 | 1.00e+00 | 4.44e-03 | 4.46e-21 | 9.64e-01 | 1.00e+00 | 8.19e-01 | 2.90e-07 | 1.10e-08 | 6.67e-02 | 8.16e-46 | 2.97e-01 | 9.18e-01 | 1.00e+00 | 8.14e-01 | 1.00e+00 | 1.00e+00 | 6.67e-02 | 9.65e-02 | 8.63e-01 | 9.63e-01 | 4.76e-66 | 9.64e-01 | 9.87e-01 | 9.64e-01 | 2.41e-02 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.69e-01 | 9.98e-01 | 1.60e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 6.72e-03 | 1.70e-10 | 9.98e-01 | 1.00e+00 | 2.64e-05 | 9.55e-01 | 9.98e-01 | 9.98e-01 | 9.65e-01 | 7.99e-29 | 9.99e-01 | 9.36e-01 | 2.06e-01 | 9.97e-01 | 1.00e+00 | 9.15e-01 | 9.99e-01 | 1.00e+00 | 8.63e-01 | 9.98e-01 | 1.00e+00 | 8.06e-01 | 1.00e+00 | 8.33e-01 | 8.87e-01 | 9.98e-01 | 9.95e-01 | 1.00e+00 | 1.02e-06 | 9.99e-01 | 5.14e-01 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 6.45e-03 | 8.56e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 4.63e-03 | 1.96e-07 | 6.34e-27 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 5.20e-01 | 1.00e+00 | 1.08e-05 | 9.87e-01 | 2.54e-08 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 9.66e-01 | 1.00e+00 | 9.94e-01 | 4.53e-02 | 0.00e+00 |
| 17 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.50e-01 | 9.97e-01 | 1.00e+00 | 9.97e-01 | 3.77e-01 | 9.97e-01 | 9.99e-01 | 8.80e-01 | 2.61e-01 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 9.99e-01 | 8.44e-01 | 9.82e-01 | 2.15e-01 | 1.42e-02 | 6.63e-01 | 1.00e+00 | 1.00e+00 | 8.84e-01 | 1.00e+00 | 3.33e-01 | 4.34e-01 | 1.00e+00 | 4.83e-01 | 1.00e+00 | 3.61e-01 | 1.00e+00 | 3.62e-08 | 1.00e+00 | 1.00e+00 | 9.24e-01 | 9.60e-01 | 1.00e+00 | 1.00e+00 | 9.30e-01 | 1.00e+00 | 8.85e-01 | 9.89e-01 | 6.44e-08 | 9.99e-01 | 6.58e-01 | 1.00e+00 | 9.99e-01 | 9.97e-01 | 3.12e-09 | 1.00e+00 | 5.14e-83 | 3.14e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.67e-01 | 4.37e-08 | 1.00e+00 | 7.85e-01 | 1.00e+00 | 2.52e-01 | 9.97e-01 | 5.62e-05 | 9.97e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.21e-01 | 9.95e-01 | 1.00e+00 | 3.24e-01 | 4.29e-11 | 1.48e-01 | 1.00e+00 | 2.20e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 3.78e-25 | 9.60e-01 | 9.60e-01 | 9.15e-05 | 2.95e-02 | 2.69e-01 | 1.00e+00 | 8.26e-01 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 9.87e-01 | 9.97e-01 | 9.97e-01 | 1.00e+00 | 9.87e-01 | 2.99e-04 | 1.00e+00 | 9.06e-01 | 3.26e-02 | 0.00e+00 | 1.00e+00 | 9.83e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.88e-01 | 9.17e-01 | 3.91e-62 | 9.01e-01 | 9.50e-01 | 1.32e-09 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.57e-51 | 1.00e+00 | 2.92e-01 | 6.66e-01 | 1.00e+00 | 5.30e-37 | 9.97e-01 | 1.00e+00 | 9.73e-01 | 9.97e-01 | 1.52e-36 | 9.96e-01 | 1.00e+00 | 9.93e-01 | 3.20e-06 | 4.69e-07 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.31e-07 | 5.46e-30 | 9.21e-01 | 1.00e+00 | 4.79e-07 | 9.85e-01 | 9.96e-01 | 9.97e-01 | 1.42e-02 | 1.00e+00 | 9.19e-01 | 1.00e+00 | 1.00e+00 | 3.86e-09 | 1.00e+00 | 9.56e-02 | 4.71e-03 | 9.01e-01 | 7.75e-01 | 1.00e+00 | 8.28e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.12e-01 | 9.89e-01 | 9.96e-01 | 9.97e-01 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 6.87e-01 | 7.72e-06 | 9.74e-01 | 9.97e-01 | 8.41e-01 | 1.00e+00 | 9.96e-01 | 9.79e-01 | 9.96e-01 | 9.86e-01 | 9.95e-01 | 9.98e-01 | 6.20e-01 | 4.08e-04 | 9.88e-01 | 5.85e-34 | 1.00e+00 | 9.97e-01 | 8.85e-01 | 9.27e-01 | 9.81e-01 | 3.10e-01 | 9.99e-01 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 8.34e-01 | 1.22e-01 | 8.35e-01 | 7.11e-01 | 1.00e+00 | 9.97e-01 | 9.93e-01 | 1.00e+00 | 9.54e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.92e-02 | 1.00e+00 | 8.36e-03 | 3.56e-24 | 9.99e-01 | 1.00e+00 | 8.65e-01 | 4.98e-12 | 8.94e-04 | 9.97e-01 | 7.77e-52 | 1.61e-03 | 1.00e+00 | 9.97e-01 | 9.98e-01 | 9.93e-01 | 9.71e-01 | 9.97e-01 | 4.05e-01 | 9.88e-07 | 9.99e-01 | 4.19e-66 | 9.28e-01 | 7.21e-01 | 8.74e-01 | 2.79e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.97e-01 | 9.68e-01 | 1.00e+00 | 2.16e-03 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 9.97e-01 | 9.28e-01 | 7.02e-01 | 1.50e-05 | 1.00e+00 | 1.00e+00 | 1.55e-05 | 5.31e-01 | 9.90e-01 | 9.99e-01 | 9.30e-01 | 1.64e-27 | 1.00e+00 | 9.99e-01 | 6.15e-03 | 1.00e+00 | 1.00e+00 | 9.04e-01 | 9.97e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 4.26e-01 | 9.97e-01 | 8.10e-02 | 4.67e-03 | 9.96e-01 | 8.20e-01 | 9.01e-01 | 8.92e-01 | 1.00e+00 | 2.34e-06 | 8.90e-01 | 9.50e-01 | 1.00e+00 | 7.87e-07 | 5.08e-11 | 1.00e+00 | 9.97e-01 | 9.99e-01 | 1.00e+00 | 9.34e-01 | 1.07e-37 | 1.81e-26 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.30e-01 | 1.00e+00 | 2.45e-01 | 1.00e+00 | 7.20e-01 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.26e-01 | 9.08e-01 | 0.00e+00 |
| 18 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.70e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 8.31e-01 | 9.28e-01 | 1.00e+00 | 1.00e+00 | 9.23e-01 | 1.00e+00 | 6.84e-01 | 9.99e-01 | 2.63e-01 | 1.97e-04 | 7.63e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.53e-01 | 6.42e-01 | 1.00e+00 | 7.86e-01 | 9.99e-01 | 7.12e-01 | 1.00e+00 | 4.28e-07 | 1.00e+00 | 1.00e+00 | 9.42e-01 | 9.86e-01 | 9.99e-01 | 1.00e+00 | 9.19e-01 | 1.00e+00 | 8.05e-01 | 9.62e-01 | 3.74e-08 | 9.52e-01 | 1.00e+00 | 1.60e-26 | 9.99e-01 | 1.00e+00 | 1.94e-11 | 1.00e+00 | 1.92e-81 | 1.46e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 2.34e-17 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 9.93e-01 | 1.26e-09 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 7.67e-11 | 9.57e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 4.24e-24 | 9.99e-01 | 9.57e-01 | 9.81e-01 | 5.64e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 5.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.71e-01 | 1.00e+00 | 1.00e+00 | 9.43e-01 | 1.14e-03 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.68e-04 | 6.47e-60 | 1.00e+00 | 6.29e-04 | 9.98e-01 | 9.53e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.76e-50 | 1.00e+00 | 2.02e-03 | 3.96e-01 | 1.00e+00 | 6.61e-36 | 1.00e+00 | 1.00e+00 | 9.44e-01 | 1.00e+00 | 1.14e-51 | 1.00e+00 | 9.86e-01 | 9.98e-01 | 3.91e-05 | 1.93e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 7.69e-08 | 2.02e-28 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.40e-03 | 1.00e+00 | 2.13e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.46e-10 | 1.00e+00 | 5.29e-01 | 4.80e-02 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.28e-10 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 2.86e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.22e-03 | 9.95e-01 | 1.00e+00 | 3.76e-12 | 1.00e+00 | 1.00e+00 | 9.42e-01 | 6.21e-01 | 9.70e-01 | 1.00e+00 | 9.98e-01 | 8.16e-01 | 9.25e-01 | 1.00e+00 | 9.80e-67 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.79e-01 | 8.45e-37 | 6.99e-03 | 1.00e+00 | 1.00e+00 | 1.50e-19 | 1.00e+00 | 7.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.50e-02 | 1.00e+00 | 4.67e-03 | 1.50e-19 | 9.99e-01 | 1.00e+00 | 9.74e-01 | 9.11e-10 | 3.31e-03 | 1.00e+00 | 1.84e-50 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.49e-03 | 9.79e-01 | 9.98e-01 | 1.00e+00 | 5.09e-02 | 2.25e-06 | 1.00e+00 | 2.67e-64 | 9.99e-01 | 1.00e+00 | 9.72e-01 | 5.96e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.41e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 9.78e-01 | 9.74e-19 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.12e-26 | 1.00e+00 | 1.00e+00 | 1.84e-02 | 1.00e+00 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 1.00e+00 | 9.40e-03 | 1.00e+00 | 1.00e+00 | 9.36e-01 | 1.00e+00 | 1.93e-02 | 8.16e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.10e-01 | 1.00e+00 | 3.03e-08 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.28e-01 | 1.00e+00 | 8.21e-01 | 3.92e-35 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.58e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.57e-07 | 6.21e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 6.20e-01 | 1.00e+00 | 0.00e+00 |
| 19 | 9.27e-02 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.62e-01 | 9.98e-01 | 1.00e+00 | 9.98e-01 | 3.11e-01 | 9.98e-01 | 1.00e+00 | 9.88e-01 | 9.88e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.96e-01 | 9.97e-01 | 3.70e-01 | 7.21e-06 | 8.00e-01 | 1.00e+00 | 1.00e+00 | 8.82e-01 | 1.00e+00 | 1.71e-01 | 4.46e-01 | 1.00e+00 | 2.58e-05 | 1.00e+00 | 1.56e-05 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 1.00e+00 | 5.44e-01 | 9.71e-01 | 1.00e+00 | 1.00e+00 | 9.01e-01 | 1.00e+00 | 8.41e-01 | 9.98e-01 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 9.97e-01 | 9.98e-01 | 8.45e-08 | 1.00e+00 | 1.95e-80 | 3.96e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.58e-01 | 3.92e-12 | 1.00e+00 | 7.57e-01 | 1.00e+00 | 1.83e-01 | 9.99e-01 | 2.76e-11 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.55e-01 | 9.95e-01 | 1.00e+00 | 9.11e-01 | 4.25e-11 | 1.08e-01 | 1.00e+00 | 1.67e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.59e-23 | 9.75e-01 | 9.97e-01 | 1.05e-04 | 3.11e-07 | 2.13e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 3.88e-01 | 8.97e-01 | 1.00e+00 | 9.98e-01 | 9.92e-01 | 8.74e-01 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 5.30e-03 | 0.00e+00 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 6.40e-59 | 9.00e-01 | 1.00e+00 | 1.64e-08 | 1.00e+00 | 1.00e+00 | 5.56e-04 | 9.92e-01 | 1.95e-49 | 1.00e+00 | 2.53e-01 | 9.93e-01 | 1.00e+00 | 2.93e-36 | 9.98e-01 | 1.00e+00 | 9.50e-01 | 9.98e-01 | 3.43e-51 | 1.00e+00 | 9.50e-01 | 1.00e+00 | 1.20e-05 | 2.22e-05 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.45e-01 | 1.00e+00 | 1.00e+00 | 2.01e-07 | 2.23e-31 | 9.22e-01 | 1.00e+00 | 9.74e-01 | 9.78e-01 | 6.92e-01 | 9.98e-01 | 1.87e-12 | 1.00e+00 | 9.50e-01 | 1.00e+00 | 1.00e+00 | 1.65e-07 | 9.98e-01 | 7.35e-01 | 8.11e-03 | 9.00e-01 | 7.54e-01 | 1.00e+00 | 7.96e-12 | 9.99e-01 | 9.98e-01 | 1.00e+00 | 9.12e-01 | 7.46e-07 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.61e-01 | 1.37e-03 | 9.56e-01 | 9.98e-01 | 1.92e-02 | 9.93e-01 | 1.49e-02 | 1.00e+00 | 1.49e-02 | 9.37e-01 | 9.83e-01 | 9.87e-01 | 8.31e-01 | 2.46e-03 | 9.82e-01 | 9.26e-39 | 1.00e+00 | 9.98e-01 | 8.83e-01 | 1.00e+00 | 9.99e-01 | 0.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.44e-02 | 1.29e-36 | 1.76e-03 | 1.00e+00 | 9.98e-01 | 9.99e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.47e-02 | 1.00e+00 | 2.72e-02 | 3.74e-25 | 1.00e+00 | 1.00e+00 | 9.14e-01 | 7.78e-12 | 2.39e-03 | 9.98e-01 | 1.46e-51 | 8.33e-01 | 1.00e+00 | 9.98e-01 | 6.44e-01 | 9.99e-01 | 8.82e-01 | 9.98e-01 | 8.84e-01 | 4.95e-06 | 1.00e+00 | 3.12e-61 | 9.57e-01 | 1.00e+00 | 1.00e+00 | 7.72e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.98e-01 | 9.62e-01 | 9.97e-01 | 3.56e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.71e-01 | 6.18e-01 | 1.15e-16 | 1.00e+00 | 1.00e+00 | 2.78e-05 | 4.87e-01 | 1.00e+00 | 1.00e+00 | 3.67e-01 | 4.48e-26 | 1.00e+00 | 1.00e+00 | 1.66e-02 | 1.00e+00 | 1.00e+00 | 2.18e-01 | 9.99e-01 | 1.00e+00 | 6.92e-01 | 1.00e+00 | 1.00e+00 | 2.57e-01 | 9.98e-01 | 7.56e-02 | 1.15e-02 | 1.00e+00 | 1.00e+00 | 9.00e-01 | 8.91e-01 | 1.00e+00 | 1.00e+00 | 8.88e-01 | 9.98e-01 | 1.00e+00 | 4.44e-06 | 6.81e-01 | 1.00e+00 | 9.98e-01 | 1.70e-01 | 1.00e+00 | 9.93e-01 | 1.76e-15 | 1.14e-24 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.90e-01 | 1.00e+00 | 8.30e-09 | 1.49e-02 | 1.00e+00 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 7.16e-01 | 9.97e-01 | 0.00e+00 |
| 20 | 9.97e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.43e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.12e-01 | 1.00e+00 | 9.98e-01 | 5.13e-01 | 3.58e-01 | 1.00e+00 | 9.45e-01 | 9.94e-01 | 1.00e+00 | 9.75e-01 | 4.25e-01 | 3.51e-02 | 3.56e-08 | 4.94e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.23e-01 | 4.71e-01 | 1.00e+00 | 2.52e-04 | 1.00e+00 | 4.70e-05 | 1.00e+00 | 6.73e-01 | 4.66e-03 | 1.00e+00 | 5.17e-01 | 1.00e+00 | 9.99e-01 | 9.97e-01 | 9.96e-01 | 9.96e-01 | 3.44e-05 | 9.98e-01 | 6.09e-01 | 3.96e-01 | 9.88e-01 | 9.83e-01 | 9.98e-01 | 1.00e+00 | 5.69e-08 | 1.00e+00 | 1.46e-78 | 1.42e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.38e-12 | 1.00e+00 | 8.26e-16 | 1.00e+00 | 5.11e-19 | 9.86e-01 | 7.38e-10 | 1.00e+00 | 1.00e+00 | 9.43e-01 | 9.98e-01 | 1.00e+00 | 9.49e-01 | 1.00e+00 | 9.87e-01 | 9.98e-01 | 1.96e-10 | 1.51e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 3.74e-23 | 9.90e-01 | 9.48e-01 | 2.48e-06 | 7.17e-01 | 1.19e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.17e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 8.85e-01 | 1.82e-03 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 | 1.00e+00 | 4.60e-05 | 1.00e+00 | 9.94e-01 | 3.53e-01 | 1.01e-57 | 1.00e+00 | 9.91e-01 | 1.99e-09 | 4.95e-01 | 1.00e+00 | 7.90e-01 | 1.00e+00 | 3.02e-49 | 1.00e+00 | 1.69e-01 | 1.73e-01 | 1.00e+00 | 2.54e-34 | 1.00e+00 | 5.23e-17 | 8.63e-01 | 1.00e+00 | 6.40e-34 | 9.87e-01 | 9.94e-01 | 9.93e-01 | 5.39e-06 | 2.90e-08 | 9.97e-01 | 1.00e+00 | 5.96e-01 | 9.61e-01 | 9.99e-01 | 1.00e+00 | 1.05e-06 | 1.08e-28 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 7.56e-01 | 1.00e+00 | 2.18e-12 | 1.00e+00 | 3.29e-07 | 1.00e+00 | 9.97e-01 | 8.48e-08 | 1.00e+00 | 9.27e-02 | 2.99e-04 | 1.00e+00 | 9.39e-08 | 1.00e+00 | 8.05e-01 | 9.97e-01 | 1.05e-05 | 1.00e+00 | 1.00e+00 | 6.88e-01 | 9.43e-01 | 1.05e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.03e-02 | 9.84e-01 | 1.05e-05 | 2.90e-02 | 1.00e+00 | 9.43e-01 | 9.94e-01 | 9.43e-01 | 8.65e-01 | 8.68e-01 | 9.75e-01 | 9.38e-01 | 7.65e-03 | 1.00e+00 | 1.85e-30 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.70e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.98e-01 | 1.00e+00 | 2.28e-04 | 5.02e-01 | 9.74e-01 | 5.76e-03 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.43e-01 | 1.00e+00 | 5.91e-01 | 1.00e+00 | 1.45e-02 | 9.71e-21 | 1.00e+00 | 1.00e+00 | 8.76e-01 | 1.48e-08 | 1.52e-06 | 1.05e-05 | 1.90e-58 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.76e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.65e-01 | 9.87e-01 | 9.98e-01 | 7.05e-61 | 9.60e-01 | 1.00e+00 | 1.00e+00 | 4.61e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 2.84e-01 | 1.55e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.77e-01 | 6.06e-01 | 2.35e-13 | 9.97e-01 | 1.00e+00 | 4.34e-06 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 9.66e-01 | 2.84e-25 | 1.00e+00 | 9.02e-01 | 3.96e-08 | 1.00e+00 | 1.00e+00 | 3.51e-01 | 1.00e+00 | 1.00e+00 | 7.56e-01 | 9.97e-01 | 1.00e+00 | 9.44e-01 | 1.00e+00 | 4.51e-02 | 8.37e-01 | 9.97e-01 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 3.28e-08 | 9.88e-01 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 6.00e-03 | 5.11e-16 | 1.63e-24 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.77e-01 | 7.37e-01 | 1.00e+00 | 4.50e-01 | 1.00e+00 | 9.60e-09 | 9.43e-01 | 8.18e-01 | 9.97e-01 | 9.99e-01 | 1.00e+00 | 9.95e-01 | 2.10e-05 | 0.00e+00 |
| 21 | 2.39e-21 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.52e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.68e-01 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 8.55e-01 | 1.00e+00 | 1.84e-01 | 1.10e-04 | 7.02e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.19e-01 | 4.91e-01 | 1.00e+00 | 8.05e-04 | 1.00e+00 | 9.20e-05 | 1.00e+00 | 9.97e-01 | 7.58e-03 | 1.00e+00 | 6.90e-04 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 9.76e-01 | 9.43e-01 | 9.98e-01 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.78e-06 | 1.00e+00 | 4.16e-77 | 2.45e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.59e-13 | 1.00e+00 | 3.04e-15 | 1.00e+00 | 3.41e-21 | 9.94e-01 | 2.48e-08 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.91e-16 | 2.10e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.29e-22 | 9.93e-01 | 9.73e-01 | 9.99e-01 | 6.80e-07 | 1.61e-01 | 1.00e+00 | 8.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.00e-01 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.31e-01 | 9.97e-01 | 1.00e+00 | 9.35e-01 | 1.25e-06 | 0.00e+00 | 1.00e+00 | 6.98e-09 | 1.00e+00 | 7.40e-01 | 1.00e+00 | 1.00e+00 | 8.66e-01 | 5.29e-01 | 1.59e-56 | 1.00e+00 | 9.73e-01 | 7.62e-01 | 2.95e-01 | 1.00e+00 | 3.04e-15 | 1.00e+00 | 1.54e-47 | 1.00e+00 | 2.07e-04 | 8.39e-01 | 1.00e+00 | 1.42e-33 | 1.00e+00 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 1.28e-53 | 3.39e-02 | 1.00e+00 | 9.97e-01 | 7.80e-06 | 9.37e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.37e-07 | 6.93e-26 | 1.00e+00 | 1.00e+00 | 3.72e-05 | 1.00e+00 | 3.39e-02 | 1.00e+00 | 4.16e-11 | 1.00e+00 | 9.76e-01 | 1.00e+00 | 1.00e+00 | 3.35e-08 | 1.00e+00 | 1.22e-01 | 1.57e-02 | 1.00e+00 | 2.38e-07 | 1.00e+00 | 2.93e-11 | 1.00e+00 | 2.15e-05 | 9.99e-01 | 1.00e+00 | 3.43e-06 | 1.00e+00 | 2.15e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.82e-03 | 9.97e-01 | 2.15e-05 | 9.69e-11 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.91e-01 | 9.09e-01 | 9.87e-01 | 9.87e-02 | 8.86e-01 | 1.00e+00 | 8.32e-65 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.35e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.13e-01 | 5.95e-01 | 2.19e-34 | 2.56e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.88e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 1.00e+00 | 4.22e-02 | 1.69e-21 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 8.50e-12 | 3.42e-08 | 2.15e-05 | 5.78e-49 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.28e-02 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 5.60e-01 | 8.11e-01 | 1.00e+00 | 6.47e-59 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.87e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.38e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.66e-01 | 8.79e-01 | 1.24e-14 | 1.00e+00 | 1.00e+00 | 7.10e-01 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 1.00e+00 | 6.97e-25 | 1.00e+00 | 1.00e+00 | 1.53e-07 | 9.24e-01 | 1.00e+00 | 3.98e-01 | 1.00e+00 | 1.00e+00 | 3.39e-02 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 7.96e-07 | 6.80e-01 | 1.00e+00 | 8.87e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.93e-01 | 1.00e+00 | 9.29e-01 | 1.00e+00 | 3.03e-06 | 9.71e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.14e-03 | 3.54e-14 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.35e-01 | 1.00e+00 | 5.30e-01 | 1.00e+00 | 2.82e-06 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.27e-05 | 0.00e+00 |
| 22 | 1.56e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.31e-01 | 1.00e+00 | 1.00e+00 | 2.88e-03 | 9.52e-01 | 1.00e+00 | 9.94e-01 | 9.88e-01 | 1.00e+00 | 6.35e-01 | 1.00e+00 | 7.33e-03 | 8.28e-04 | 8.71e-01 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 6.34e-01 | 4.17e-01 | 1.00e+00 | 7.73e-01 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 5.90e-01 | 1.12e-02 | 1.00e+00 | 4.09e-03 | 9.32e-01 | 3.60e-01 | 1.00e+00 | 8.61e-01 | 1.00e+00 | 9.59e-01 | 9.56e-01 | 8.69e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.37e-07 | 1.00e+00 | 8.91e-76 | 3.03e-03 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 2.39e-21 | 1.00e+00 | 1.07e-14 | 9.91e-01 | 5.82e-19 | 1.00e+00 | 6.75e-11 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 1.00e+00 | 6.81e-01 | 1.10e-10 | 1.46e-01 | 1.00e+00 | 5.61e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.95e-22 | 5.69e-01 | 9.80e-01 | 1.11e-03 | 3.99e-05 | 4.87e-02 | 1.00e+00 | 9.24e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.95e-01 | 7.39e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.36e-01 | 9.99e-01 | 1.00e+00 | 9.78e-01 | 4.81e-05 | 0.00e+00 | 1.00e+00 | 9.89e-01 | 9.99e-01 | 9.85e-01 | 1.00e+00 | 1.00e+00 | 8.26e-01 | 6.97e-02 | 2.30e-55 | 9.89e-01 | 9.94e-01 | 1.34e-09 | 3.13e-01 | 1.00e+00 | 2.97e-01 | 1.00e+00 | 2.58e-46 | 1.00e+00 | 1.87e-01 | 7.01e-01 | 1.00e+00 | 7.18e-33 | 1.00e+00 | 9.97e-01 | 9.84e-01 | 1.00e+00 | 1.76e-37 | 9.76e-01 | 1.00e+00 | 9.88e-01 | 6.49e-11 | 2.09e-06 | 4.88e-02 | 1.00e+00 | 9.52e-01 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 8.14e-07 | 1.31e-25 | 9.94e-01 | 1.00e+00 | 1.49e-04 | 9.99e-01 | 4.88e-02 | 1.00e+00 | 7.97e-01 | 1.00e+00 | 9.31e-01 | 1.00e+00 | 1.00e+00 | 2.63e-08 | 1.00e+00 | 3.79e-01 | 3.54e-04 | 9.89e-01 | 7.11e-07 | 1.00e+00 | 2.12e-12 | 1.00e+00 | 4.28e-05 | 9.98e-01 | 9.91e-01 | 1.84e-01 | 9.90e-01 | 4.28e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.28e-01 | 7.17e-03 | 7.47e-01 | 4.28e-05 | 2.67e-10 | 1.00e+00 | 9.90e-01 | 9.90e-01 | 9.90e-01 | 9.48e-01 | 9.48e-01 | 9.64e-01 | 6.86e-01 | 6.37e-06 | 9.99e-01 | 7.45e-64 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 3.38e-01 | 0.00e+00 | 1.00e+00 | 9.76e-01 | 1.00e+00 | 1.00e+00 | 9.29e-01 | 2.79e-01 | 3.61e-04 | 1.35e-02 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 8.00e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.64e-04 | 1.43e-22 | 1.00e+00 | 1.00e+00 | 5.39e-01 | 1.18e-10 | 2.46e-06 | 4.28e-05 | 5.20e-47 | 6.54e-01 | 9.88e-01 | 1.00e+00 | 3.53e-02 | 9.76e-01 | 1.00e+00 | 1.00e+00 | 8.11e-01 | 9.90e-01 | 1.00e+00 | 1.21e-58 | 6.02e-01 | 1.00e+00 | 1.00e+00 | 1.82e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 5.74e-01 | 1.29e-05 | 1.00e+00 | 1.00e+00 | 9.76e-01 | 1.00e+00 | 9.98e-01 | 7.41e-01 | 1.36e-14 | 1.00e+00 | 1.00e+00 | 4.89e-08 | 8.49e-01 | 9.72e-01 | 1.00e+00 | 9.98e-01 | 5.87e-24 | 1.00e+00 | 1.00e+00 | 1.06e-06 | 9.33e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 4.88e-02 | 1.00e+00 | 1.00e+00 | 5.88e-01 | 1.00e+00 | 7.86e-04 | 8.98e-01 | 4.88e-02 | 9.20e-01 | 9.89e-01 | 9.88e-01 | 1.00e+00 | 9.36e-01 | 9.87e-01 | 9.97e-01 | 1.00e+00 | 9.82e-05 | 6.60e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.61e-03 | 2.70e-14 | 9.62e-23 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.06e-01 | 1.00e+00 | 1.90e-01 | 1.00e+00 | 1.71e-05 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.09e-05 | 0.00e+00 |
| 23 | 6.57e-01 | 1.00e+00 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 9.39e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.39e-01 | 1.00e+00 | 1.00e+00 | 3.57e-01 | 2.07e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 6.93e-01 | 9.83e-01 | 1.03e-01 | 3.01e-05 | 7.87e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.63e-01 | 3.40e-01 | 1.00e+00 | 6.20e-04 | 1.00e+00 | 3.13e-04 | 1.00e+00 | 9.93e-01 | 1.77e-02 | 9.85e-01 | 7.58e-02 | 4.31e-02 | 8.10e-01 | 9.85e-01 | 9.14e-01 | 1.00e+00 | 6.13e-03 | 9.96e-01 | 9.98e-01 | 9.72e-01 | 9.80e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.41e-08 | 1.00e+00 | 1.94e-78 | 1.16e-01 | 0.00e+00 | 1.00e+00 | 9.85e-01 | 1.00e+00 | 3.18e-12 | 1.00e+00 | 2.91e-14 | 1.00e+00 | 2.07e-17 | 9.97e-01 | 2.74e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.68e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.23e-10 | 6.00e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.90e-21 | 9.00e-01 | 8.96e-01 | 3.56e-08 | 2.54e-06 | 2.43e-01 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.39e-01 | 9.33e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.88e-01 | 4.30e-07 | 0.00e+00 | 1.00e+00 | 9.98e-01 | 9.92e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.11e-01 | 3.54e-54 | 1.00e+00 | 9.57e-01 | 8.98e-09 | 6.88e-01 | 1.00e+00 | 4.09e-01 | 1.00e+00 | 7.37e-46 | 1.00e+00 | 4.99e-02 | 4.68e-01 | 1.00e+00 | 6.62e-32 | 1.00e+00 | 9.99e-01 | 9.49e-01 | 1.00e+00 | 9.81e-47 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.08e-11 | 4.60e-08 | 4.09e-01 | 1.00e+00 | 9.93e-01 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 1.78e-06 | 5.50e-26 | 7.76e-01 | 9.85e-01 | 9.93e-01 | 1.00e+00 | 8.94e-01 | 1.00e+00 | 7.42e-11 | 1.00e+00 | 6.87e-01 | 1.00e+00 | 9.85e-01 | 6.70e-08 | 1.00e+00 | 2.37e-03 | 3.19e-03 | 1.00e+00 | 8.89e-07 | 1.00e+00 | 6.91e-12 | 4.81e-01 | 8.24e-05 | 6.60e-14 | 1.00e+00 | 1.63e-05 | 1.00e+00 | 8.24e-05 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 3.60e-02 | 9.97e-01 | 8.24e-05 | 8.33e-02 | 1.00e+00 | 4.09e-01 | 9.60e-01 | 1.00e+00 | 9.96e-01 | 9.45e-01 | 9.99e-01 | 9.94e-01 | 7.47e-06 | 1.00e+00 | 1.66e-62 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.72e-01 | 0.00e+00 | 1.00e+00 | 9.85e-01 | 9.99e-01 | 1.00e+00 | 4.39e-27 | 6.47e-01 | 3.50e-33 | 2.11e-02 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 2.15e-05 | 2.11e-20 | 9.84e-01 | 1.00e+00 | 9.90e-01 | 1.07e-11 | 2.14e-09 | 8.24e-05 | 7.44e-52 | 1.00e+00 | 9.22e-01 | 1.00e+00 | 8.54e-01 | 9.86e-01 | 9.96e-01 | 1.00e+00 | 3.19e-01 | 1.00e+00 | 9.93e-01 | 7.35e-57 | 9.00e-01 | 1.00e+00 | 1.00e+00 | 1.98e-01 | 1.00e+00 | 9.82e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 3.90e-07 | 1.00e+00 | 4.09e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.36e-01 | 5.82e-13 | 1.00e+00 | 1.00e+00 | 1.46e-07 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.99e-01 | 7.45e-25 | 1.00e+00 | 9.99e-01 | 6.65e-07 | 1.00e+00 | 1.00e+00 | 5.89e-01 | 1.00e+00 | 1.00e+00 | 8.94e-01 | 1.00e+00 | 1.00e+00 | 9.63e-01 | 1.00e+00 | 1.42e-01 | 9.88e-01 | 4.09e-01 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 9.17e-01 | 1.00e+00 | 1.92e-06 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 4.73e-02 | 3.07e-13 | 1.44e-22 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.57e-01 | 1.00e+00 | 6.40e-01 | 1.00e+00 | 1.89e-07 | 1.00e+00 | 9.01e-01 | 9.85e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 5.22e-05 | 0.00e+00 |
| 24 | 7.19e-01 | 9.99e-01 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 1.74e-01 | 9.99e-01 | 1.00e+00 | 9.65e-01 | 9.90e-01 | 9.91e-01 | 9.96e-01 | 9.08e-01 | 1.00e+00 | 9.09e-01 | 1.00e+00 | 1.21e-02 | 4.28e-11 | 7.27e-01 | 1.00e+00 | 9.73e-01 | 9.04e-01 | 9.99e-01 | 8.55e-02 | 8.57e-01 | 1.00e+00 | 1.07e-03 | 9.59e-01 | 3.52e-04 | 1.00e+00 | 1.40e-06 | 2.42e-02 | 1.00e+00 | 9.55e-01 | 9.09e-01 | 9.98e-01 | 9.91e-01 | 6.23e-01 | 1.00e+00 | 8.16e-01 | 9.03e-01 | 1.05e-06 | 8.70e-01 | 1.00e+00 | 7.00e-01 | 9.98e-01 | 9.99e-01 | 7.84e-13 | 1.00e+00 | 4.72e-73 | 3.23e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.57e-01 | 2.59e-15 | 9.99e-01 | 6.25e-14 | 9.97e-01 | 8.90e-18 | 9.94e-01 | 1.87e-12 | 9.99e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.41e-01 | 9.76e-01 | 1.00e+00 | 8.91e-01 | 3.50e-09 | 4.59e-02 | 9.91e-01 | 1.05e-01 | 1.00e+00 | 1.00e+00 | 9.22e-01 | 1.00e+00 | 1.45e-20 | 9.66e-01 | 9.94e-01 | 1.06e-03 | 7.01e-07 | 2.83e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 6.99e-01 | 9.63e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 5.69e-01 | 1.00e+00 | 1.00e+00 | 9.70e-01 | 1.18e-02 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.49e-01 | 2.26e-53 | 9.21e-01 | 9.82e-01 | 1.17e-07 | 3.95e-01 | 1.00e+00 | 4.03e-02 | 1.00e+00 | 2.04e-44 | 9.91e-01 | 1.73e-01 | 2.19e-01 | 1.00e+00 | 2.75e-31 | 9.99e-01 | 9.78e-01 | 9.38e-01 | 9.99e-01 | 6.68e-46 | 1.00e+00 | 9.88e-01 | 9.92e-01 | 1.13e-10 | 7.49e-06 | 1.00e+00 | 9.91e-01 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.92e-06 | 8.04e-25 | 1.00e+00 | 1.00e+00 | 9.46e-01 | 9.90e-01 | 9.32e-02 | 9.99e-01 | 6.42e-10 | 9.91e-01 | 1.54e-01 | 1.00e+00 | 1.00e+00 | 2.65e-08 | 1.00e+00 | 3.97e-02 | 2.42e-02 | 9.21e-01 | 1.37e-06 | 1.00e+00 | 1.96e-11 | 1.00e+00 | 1.54e-04 | 9.98e-01 | 9.33e-01 | 1.13e-05 | 1.00e+00 | 1.54e-04 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 6.55e-01 | 4.20e-02 | 8.70e-01 | 1.54e-04 | 1.86e-09 | 1.00e+00 | 4.77e-01 | 8.86e-01 | 4.77e-01 | 9.84e-01 | 9.50e-01 | 9.87e-01 | 6.48e-01 | 6.59e-04 | 9.92e-01 | 2.57e-61 | 9.88e-01 | 9.99e-01 | 9.05e-01 | 1.00e+00 | 1.16e-01 | 0.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 7.40e-02 | 4.71e-32 | 3.18e-02 | 1.00e+00 | 9.99e-01 | 7.88e-01 | 1.00e+00 | 9.53e-01 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 1.00e+00 | 1.00e+00 | 8.26e-03 | 1.16e-18 | 9.73e-01 | 9.99e-01 | 8.44e-01 | 2.54e-11 | 3.29e-07 | 1.54e-04 | 6.98e-47 | 7.13e-01 | 1.00e+00 | 9.99e-01 | 1.67e-02 | 9.98e-01 | 9.96e-01 | 9.99e-01 | 7.84e-01 | 1.00e+00 | 1.00e+00 | 9.82e-57 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 8.82e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 4.36e-01 | 1.00e+00 | 2.07e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.31e-01 | 5.48e-01 | 1.35e-13 | 1.00e+00 | 9.91e-01 | 8.56e-06 | 4.48e-01 | 9.89e-01 | 1.00e+00 | 9.86e-01 | 1.05e-22 | 1.00e+00 | 1.00e+00 | 7.25e-07 | 9.88e-01 | 1.00e+00 | 7.01e-01 | 1.00e+00 | 1.00e+00 | 9.32e-02 | 1.00e+00 | 1.00e+00 | 1.10e-01 | 9.99e-01 | 1.89e-03 | 9.82e-01 | 1.00e+00 | 1.00e+00 | 9.21e-01 | 9.13e-01 | 1.00e+00 | 9.96e-01 | 9.10e-01 | 8.68e-01 | 1.00e+00 | 3.20e-05 | 7.78e-01 | 1.00e+00 | 9.99e-01 | 8.60e-01 | 1.00e+00 | 2.14e-02 | 6.20e-32 | 1.10e-21 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 9.93e-02 | 1.00e+00 | 6.69e-05 | 4.77e-01 | 9.99e-01 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.12e-04 | 0.00e+00 |
| 25 | 1.34e-19 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.60e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 8.68e-01 | 1.00e+00 | 1.00e+00 | 9.67e-01 | 1.00e+00 | 9.75e-01 | 9.99e-01 | 2.02e-01 | 4.99e-04 | 7.93e-01 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.16e-01 | 4.34e-01 | 1.00e+00 | 9.16e-01 | 1.00e+00 | 2.54e-38 | 1.00e+00 | 3.91e-06 | 3.54e-02 | 1.00e+00 | 5.94e-02 | 8.43e-01 | 9.93e-01 | 9.99e-01 | 9.90e-01 | 1.00e+00 | 9.45e-01 | 9.83e-01 | 8.00e-06 | 9.75e-01 | 9.25e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.98e-12 | 9.46e-01 | 1.23e-71 | 1.70e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 2.21e-14 | 1.00e+00 | 1.61e-13 | 1.00e+00 | 4.69e-19 | 9.78e-01 | 1.77e-09 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.67e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.71e-16 | 4.52e-01 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 6.25e-20 | 1.00e+00 | 9.92e-01 | 1.18e-03 | 1.64e-06 | 2.89e-01 | 9.65e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 8.75e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 1.00e+00 | 1.00e+00 | 9.68e-01 | 5.42e-08 | 0.00e+00 | 1.00e+00 | 2.17e-07 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.61e-01 | 3.79e-53 | 1.00e+00 | 8.35e-01 | 6.84e-09 | 5.12e-01 | 1.00e+00 | 2.21e-13 | 1.00e+00 | 4.01e-44 | 1.00e+00 | 3.98e-04 | 4.56e-01 | 1.00e+00 | 1.40e-31 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 3.69e-45 | 1.00e+00 | 9.48e-01 | 7.75e-01 | 1.05e-05 | 1.80e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.31e-01 | 1.00e+00 | 1.00e+00 | 1.52e-05 | 8.12e-28 | 1.00e+00 | 1.00e+00 | 3.12e-02 | 1.00e+00 | 5.45e-01 | 1.00e+00 | 4.30e-02 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.71e-05 | 9.99e-01 | 1.13e-01 | 3.43e-02 | 1.00e+00 | 4.54e-06 | 1.00e+00 | 3.25e-09 | 1.00e+00 | 2.80e-04 | 1.00e+00 | 1.00e+00 | 2.84e-05 | 1.00e+00 | 2.80e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.26e-02 | 9.98e-01 | 2.80e-04 | 3.71e-04 | 1.00e+00 | 1.00e+00 | 9.64e-01 | 8.34e-01 | 9.30e-01 | 9.40e-01 | 7.54e-01 | 1.09e-02 | 1.20e-04 | 1.00e+00 | 2.70e-60 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.06e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.81e-01 | 4.78e-01 | 7.64e-32 | 4.14e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 1.25e-03 | 9.31e-21 | 1.00e+00 | 1.00e+00 | 9.04e-01 | 2.40e-15 | 5.67e-08 | 2.80e-04 | 8.85e-46 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.11e-01 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 7.57e-01 | 8.34e-01 | 1.00e+00 | 2.40e-56 | 9.12e-01 | 9.46e-01 | 9.18e-01 | 3.43e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.81e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.41e-01 | 3.62e-12 | 1.00e+00 | 1.00e+00 | 3.32e-05 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 8.62e-01 | 8.61e-23 | 9.98e-01 | 1.00e+00 | 2.43e-06 | 9.84e-01 | 1.00e+00 | 6.01e-03 | 1.00e+00 | 9.99e-01 | 5.45e-01 | 1.00e+00 | 1.00e+00 | 9.13e-01 | 1.00e+00 | 1.32e-04 | 7.71e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 1.00e+00 | 9.79e-01 | 1.00e+00 | 8.85e-05 | 9.85e-01 | 1.00e+00 | 1.00e+00 | 9.35e-01 | 1.00e+00 | 2.08e-02 | 2.56e-30 | 9.14e-22 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.29e-01 | 1.00e+00 | 6.69e-01 | 9.46e-01 | 5.34e-05 | 8.34e-01 | 1.00e+00 | 9.99e-01 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 1.30e-05 | 0.00e+00 |
| 26 | 8.17e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 8.57e-01 | 1.00e+00 | 1.00e+00 | 9.63e-01 | 1.00e+00 | 9.63e-01 | 1.00e+00 | 6.68e-05 | 9.99e-01 | 1.00e+00 | 9.98e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 1.86e-02 | 5.80e-07 | 3.80e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.40e-01 | 4.53e-03 | 1.00e+00 | 6.12e-03 | 9.99e-01 | 1.51e-03 | 9.97e-01 | 7.71e-05 | 9.97e-01 | 1.00e+00 | 5.27e-01 | 9.99e-01 | 1.19e-12 | 1.00e+00 | 9.39e-01 | 1.00e+00 | 5.06e-01 | 1.00e+00 | 1.09e-07 | 9.99e-01 | 9.99e-01 | 3.98e-21 | 1.00e+00 | 1.00e+00 | 7.63e-14 | 1.00e+00 | 3.21e-70 | 2.25e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 2.81e-12 | 1.00e+00 | 6.51e-01 | 1.00e+00 | 1.36e-03 | 9.76e-01 | 7.22e-13 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 8.43e-01 | 1.00e+00 | 9.98e-01 | 5.23e-10 | 3.46e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.51e-19 | 1.00e+00 | 9.94e-01 | 1.08e-03 | 1.20e-05 | 8.75e-01 | 1.00e+00 | 2.96e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.06e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.89e-01 | 1.00e+00 | 9.25e-01 | 3.21e-04 | 0.00e+00 | 1.00e+00 | 8.05e-01 | 1.00e+00 | 1.56e-04 | 1.00e+00 | 1.00e+00 | 9.56e-01 | 1.46e-10 | 2.39e-51 | 1.00e+00 | 1.69e-02 | 6.28e-10 | 9.99e-01 | 1.00e+00 | 9.55e-01 | 1.00e+00 | 1.44e-42 | 1.00e+00 | 4.32e-03 | 6.89e-01 | 1.00e+00 | 8.57e-30 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 1.00e+00 | 1.27e-78 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 4.72e-09 | 7.89e-08 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.87e-01 | 1.00e+00 | 1.00e+00 | 1.47e-05 | 4.07e-25 | 1.00e+00 | 1.00e+00 | 9.83e-03 | 1.00e+00 | 8.74e-01 | 9.63e-01 | 5.99e-12 | 1.00e+00 | 9.30e-01 | 1.00e+00 | 1.00e+00 | 5.05e-10 | 1.00e+00 | 8.15e-01 | 1.94e-02 | 1.00e+00 | 3.58e-01 | 1.00e+00 | 4.28e-08 | 1.00e+00 | 9.63e-01 | 1.00e+00 | 2.71e-01 | 4.95e-05 | 9.97e-01 | 9.63e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.72e-03 | 1.00e+00 | 9.63e-01 | 3.28e-02 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.97e-01 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 8.73e-01 | 1.36e-04 | 7.20e-01 | 6.28e-58 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.26e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 2.71e-01 | 5.66e-01 | 6.86e-31 | 3.77e-03 | 1.00e+00 | 1.00e+00 | 6.28e-15 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 2.90e-05 | 1.00e+00 | 3.78e-02 | 2.94e-22 | 1.00e+00 | 1.00e+00 | 9.81e-01 | 6.84e-12 | 1.21e-06 | 9.63e-01 | 2.84e-47 | 1.39e-05 | 1.00e+00 | 1.00e+00 | 8.08e-01 | 1.00e+00 | 1.00e+00 | 9.63e-01 | 2.49e-01 | 0.00e+00 | 1.00e+00 | 3.62e-54 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 3.24e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.63e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.67e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 9.04e-01 | 1.62e-18 | 1.00e+00 | 1.00e+00 | 2.42e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 2.39e-21 | 1.00e+00 | 1.00e+00 | 4.27e-05 | 1.00e+00 | 1.00e+00 | 6.12e-01 | 1.00e+00 | 1.00e+00 | 8.74e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.37e-03 | 3.08e-10 | 1.00e+00 | 2.71e-01 | 1.00e+00 | 2.09e-01 | 1.00e+00 | 1.19e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.75e-07 | 1.43e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.25e-01 | 1.03e-29 | 5.63e-21 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.95e-01 | 1.00e+00 | 3.39e-01 | 1.00e+00 | 8.63e-07 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.22e-01 | 9.71e-01 | 0.00e+00 |
| 27 | 4.61e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.70e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.82e-01 | 6.73e-01 | 9.96e-01 | 7.75e-05 | 7.30e-02 | 1.00e+00 | 9.99e-01 | 9.97e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.71e-01 | 3.49e-06 | 6.74e-01 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 7.56e-01 | 2.43e-01 | 1.00e+00 | 1.18e-02 | 9.99e-01 | 2.58e-03 | 9.94e-01 | 3.00e-06 | 9.25e-01 | 1.00e+00 | 1.31e-02 | 3.65e-01 | 2.71e-12 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 4.60e-01 | 9.96e-01 | 5.98e-08 | 8.97e-01 | 1.00e+00 | 1.00e+00 | 9.61e-01 | 1.00e+00 | 5.00e-10 | 1.00e+00 | 2.16e-69 | 1.32e-05 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.85e-01 | 2.05e-12 | 1.00e+00 | 7.83e-02 | 1.00e+00 | 2.03e-11 | 9.07e-01 | 7.59e-15 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 6.82e-01 | 2.56e-01 | 1.00e+00 | 8.74e-01 | 7.52e-09 | 3.04e-02 | 1.00e+00 | 9.75e-01 | 9.98e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 1.25e-18 | 9.19e-01 | 9.99e-01 | 2.72e-03 | 1.38e-06 | 3.48e-01 | 1.00e+00 | 7.99e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 8.29e-01 | 3.77e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.47e-03 | 1.00e+00 | 9.69e-01 | 1.64e-05 | 0.00e+00 | 1.00e+00 | 9.80e-01 | 9.97e-01 | 9.23e-01 | 4.99e-04 | 1.00e+00 | 7.35e-01 | 2.44e-02 | 7.56e-50 | 1.00e+00 | 9.99e-01 | 2.44e-08 | 9.89e-01 | 1.00e+00 | 1.62e-02 | 1.00e+00 | 1.98e-42 | 1.00e+00 | 2.84e-01 | 8.72e-01 | 1.00e+00 | 2.94e-29 | 1.00e+00 | 2.45e-14 | 9.82e-01 | 1.00e+00 | 2.83e-43 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 4.31e-09 | 2.24e-08 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 1.36e-04 | 2.47e-23 | 9.99e-01 | 1.00e+00 | 2.74e-07 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 9.13e-10 | 1.00e+00 | 1.27e-05 | 9.99e-01 | 1.00e+00 | 5.44e-08 | 1.00e+00 | 2.96e-01 | 1.04e-02 | 1.00e+00 | 6.87e-03 | 1.00e+00 | 7.91e-10 | 1.00e+00 | 6.73e-01 | 1.00e+00 | 9.99e-01 | 1.13e-04 | 1.00e+00 | 6.73e-01 | 1.00e+00 | 9.70e-01 | 1.00e+00 | 1.00e+00 | 3.12e-03 | 9.76e-01 | 6.73e-01 | 2.35e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.97e-01 | 9.94e-01 | 6.48e-01 | 9.09e-01 | 3.08e-02 | 1.16e-01 | 4.73e-57 | 9.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.52e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.92e-01 | 1.00e+00 | 8.18e-01 | 1.59e-02 | 4.22e-30 | 6.72e-02 | 2.41e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.15e-01 | 9.99e-01 | 3.84e-02 | 8.72e-22 | 9.98e-01 | 9.98e-01 | 9.94e-01 | 4.40e-10 | 1.19e-08 | 6.73e-01 | 2.80e-45 | 9.58e-03 | 9.99e-01 | 1.00e+00 | 9.60e-01 | 1.00e+00 | 1.00e+00 | 6.73e-01 | 3.38e-01 | 8.54e-04 | 8.89e-01 | 8.80e-53 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 2.74e-02 | 1.00e+00 | 2.87e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 2.65e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.70e-01 | 3.33e-01 | 3.38e-17 | 1.00e+00 | 1.00e+00 | 1.42e-05 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.64e-26 | 1.00e+00 | 1.00e+00 | 1.43e-01 | 1.00e+00 | 1.00e+00 | 7.94e-01 | 9.99e-01 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 5.59e-02 | 7.58e-02 | 1.00e+00 | 7.87e-01 | 1.00e+00 | 1.57e-03 | 1.00e+00 | 3.39e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.88e-06 | 2.33e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.07e-02 | 8.25e-30 | 2.42e-20 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.16e-01 | 1.00e+00 | 5.36e-03 | 1.00e+00 | 1.02e-08 | 1.00e+00 | 9.36e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 8.27e-01 | 6.11e-01 | 0.00e+00 |
| 28 | 3.21e-17 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.68e-01 | 1.00e+00 | 2.48e-02 | 7.31e-01 | 8.80e-01 | 9.99e-01 | 9.62e-01 | 1.00e+00 | 1.00e+00 | 9.78e-01 | 9.93e-01 | 9.87e-01 | 9.99e-01 | 3.56e-02 | 1.09e-04 | 2.42e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.52e-01 | 9.67e-01 | 1.00e+00 | 2.06e-02 | 2.70e-01 | 3.69e-03 | 1.00e+00 | 1.47e-05 | 8.98e-01 | 1.00e+00 | 4.07e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.96e-01 | 1.00e+00 | 3.11e-02 | 9.86e-01 | 5.93e-05 | 1.00e+00 | 1.00e+00 | 1.37e-20 | 1.00e+00 | 1.00e+00 | 1.05e-08 | 9.99e-01 | 3.14e-77 | 6.45e-01 | 0.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.48e-11 | 1.00e+00 | 8.86e-02 | 1.00e+00 | 1.77e-11 | 8.43e-01 | 9.46e-08 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 7.85e-01 | 3.44e-01 | 1.00e+00 | 9.06e-01 | 2.73e-13 | 1.22e-02 | 1.00e+00 | 9.84e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 2.83e-18 | 1.00e+00 | 9.99e-01 | 3.32e-07 | 2.76e-04 | 3.45e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.68e-01 | 2.06e-02 | 9.75e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.54e-01 | 1.00e+00 | 1.00e+00 | 9.76e-01 | 8.89e-06 | 0.00e+00 | 9.99e-01 | 2.86e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.86e-03 | 2.05e-49 | 1.00e+00 | 1.12e-03 | 3.57e-06 | 9.73e-01 | 1.00e+00 | 2.30e-11 | 1.00e+00 | 6.78e-41 | 1.00e+00 | 2.98e-03 | 9.76e-01 | 1.00e+00 | 2.36e-29 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 1.00e+00 | 5.12e-42 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.48e-08 | 1.11e-10 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.79e-01 | 3.44e-01 | 7.03e-07 | 9.14e-21 | 1.59e-02 | 1.00e+00 | 9.44e-01 | 9.96e-01 | 2.52e-01 | 1.00e+00 | 2.22e-08 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 2.03e-07 | 1.00e+00 | 6.66e-02 | 1.64e-03 | 1.00e+00 | 6.41e-03 | 9.99e-01 | 8.91e-13 | 9.77e-01 | 7.31e-01 | 1.63e-18 | 1.00e+00 | 8.52e-05 | 1.00e+00 | 7.31e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.13e-01 | 9.98e-01 | 7.31e-01 | 6.36e-08 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 6.19e-02 | 1.47e-03 | 9.20e-02 | 2.16e-55 | 9.86e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.68e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.03e-01 | 1.00e+00 | 3.39e-24 | 1.22e-02 | 9.23e-35 | 1.59e-01 | 1.00e+00 | 1.00e+00 | 5.38e-15 | 9.99e-01 | 1.70e-05 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.16e-15 | 7.17e-03 | 6.00e-23 | 1.00e+00 | 1.00e+00 | 9.79e-01 | 2.55e-10 | 1.21e-06 | 7.31e-01 | 8.48e-47 | 8.93e-01 | 9.99e-01 | 1.00e+00 | 1.42e-01 | 1.00e+00 | 1.00e+00 | 7.31e-01 | 1.27e-01 | 1.00e+00 | 9.43e-01 | 1.77e-51 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.39e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 | 1.53e-07 | 1.00e+00 | 1.43e-03 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 2.31e-01 | 2.34e-11 | 1.00e+00 | 1.00e+00 | 7.26e-06 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.54e-20 | 8.02e-01 | 1.00e+00 | 3.25e-05 | 1.95e-02 | 1.00e+00 | 6.31e-01 | 1.00e+00 | 1.00e+00 | 2.52e-01 | 1.00e+00 | 9.99e-01 | 9.80e-01 | 1.00e+00 | 5.32e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.57e-03 | 1.00e+00 | 9.51e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 3.66e-06 | 9.92e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.05e-01 | 1.21e-27 | 1.48e-18 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.19e-01 | 9.99e-01 | 8.30e-03 | 1.00e+00 | 2.08e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.59e-01 | 1.00e+00 | 1.00e+00 | 7.17e-01 | 0.00e+00 |
| 29 | 2.16e-17 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.73e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.37e-01 | 1.00e+00 | 1.00e+00 | 5.73e-05 | 9.05e-01 | 1.00e+00 | 9.95e-01 | 9.62e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 4.93e-01 | 2.10e-05 | 4.68e-01 | 1.00e+00 | 1.00e+00 | 9.65e-01 | 1.00e+00 | 3.98e-01 | 2.55e-01 | 1.00e+00 | 2.36e-02 | 1.00e+00 | 4.35e-03 | 1.00e+00 | 1.08e-04 | 8.12e-02 | 1.00e+00 | 7.78e-02 | 9.80e-01 | 9.99e-12 | 1.00e+00 | 8.91e-01 | 1.00e+00 | 6.79e-04 | 9.98e-01 | 2.40e-07 | 6.90e-01 | 9.99e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.52e-08 | 9.99e-01 | 1.31e-67 | 1.38e-05 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.79e-01 | 7.69e-11 | 1.00e+00 | 8.37e-12 | 1.00e+00 | 2.46e-15 | 9.83e-01 | 1.32e-12 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.79e-01 | 9.98e-01 | 1.00e+00 | 9.24e-01 | 7.23e-13 | 1.24e-01 | 1.00e+00 | 1.69e-01 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 9.97e-01 | 5.61e-18 | 6.33e-01 | 9.69e-01 | 2.67e-06 | 7.17e-08 | 3.67e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 9.99e-01 | 9.96e-01 | 1.81e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.77e-01 | 4.90e-02 | 9.99e-01 | 1.00e+00 | 1.85e-06 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.77e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.23e-03 | 5.45e-49 | 9.74e-01 | 9.96e-01 | 2.65e-08 | 8.51e-01 | 1.00e+00 | 6.98e-15 | 1.00e+00 | 5.54e-40 | 1.00e+00 | 5.92e-03 | 9.33e-01 | 1.00e+00 | 5.12e-28 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 3.81e-45 | 3.05e-01 | 9.17e-01 | 7.80e-01 | 1.01e-09 | 1.89e-10 | 1.00e+00 | 1.00e+00 | 8.22e-01 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 7.13e-05 | 7.17e-23 | 9.83e-01 | 1.00e+00 | 5.85e-04 | 1.00e+00 | 9.51e-01 | 1.00e+00 | 8.30e-09 | 1.00e+00 | 3.66e-01 | 1.00e+00 | 1.00e+00 | 4.49e-08 | 1.00e+00 | 1.90e-03 | 7.50e-04 | 9.74e-01 | 1.95e-05 | 1.00e+00 | 1.47e-08 | 1.00e+00 | 2.34e-03 | 9.98e-01 | 9.79e-01 | 3.30e-05 | 1.00e+00 | 2.34e-03 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 7.99e-01 | 3.67e-03 | 4.76e-01 | 2.34e-03 | 8.61e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.40e-01 | 7.67e-01 | 2.91e-01 | 5.21e-02 | 9.95e-01 | 3.33e-56 | 1.00e+00 | 1.00e+00 | 9.66e-01 | 1.00e+00 | 5.29e-01 | 0.00e+00 | 9.99e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.35e-24 | 5.35e-01 | 6.74e-29 | 5.93e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.82e-01 | 1.00e+00 | 7.97e-02 | 1.89e-21 | 9.99e-01 | 1.00e+00 | 9.61e-01 | 9.87e-10 | 2.78e-08 | 2.34e-03 | 4.75e-47 | 3.80e-04 | 9.70e-01 | 1.00e+00 | 8.43e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 8.95e-01 | 2.34e-03 | 9.25e-01 | 9.81e-51 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 2.09e-04 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 4.59e-06 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.53e-01 | 8.61e-01 | 6.98e-15 | 1.00e+00 | 1.00e+00 | 1.64e-04 | 6.05e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 6.38e-20 | 1.00e+00 | 1.00e+00 | 2.23e-01 | 1.00e+00 | 1.00e+00 | 7.40e-01 | 1.00e+00 | 1.00e+00 | 9.51e-01 | 1.00e+00 | 1.00e+00 | 2.04e-01 | 1.00e+00 | 2.61e-05 | 3.27e-02 | 1.00e+00 | 1.00e+00 | 9.74e-01 | 9.70e-01 | 1.00e+00 | 2.79e-04 | 9.68e-01 | 1.00e+00 | 1.00e+00 | 6.71e-04 | 1.28e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.17e-02 | 2.56e-28 | 4.89e-19 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.66e-01 | 1.00e+00 | 2.39e-01 | 9.99e-01 | 6.96e-08 | 1.00e+00 | 9.69e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 9.48e-01 | 7.98e-04 | 0.00e+00 |
| 30 | 3.40e-16 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.78e-01 | 1.00e+00 | 1.00e+00 | 1.67e-03 | 9.76e-01 | 9.66e-01 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 2.00e-02 | 2.21e-05 | 9.54e-01 | 1.00e+00 | 3.63e-01 | 1.00e+00 | 1.84e-01 | 2.02e-01 | 8.24e-01 | 1.00e+00 | 4.08e-02 | 1.00e+00 | 9.38e-03 | 1.00e+00 | 8.90e-05 | 1.51e-01 | 1.00e+00 | 3.30e-03 | 1.00e+00 | 2.29e-11 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.86e-03 | 9.98e-01 | 1.35e-08 | 9.99e-01 | 9.90e-01 | 1.45e-20 | 1.00e+00 | 1.00e+00 | 1.58e-10 | 9.99e-01 | 1.86e-68 | 1.54e-01 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 6.14e-10 | 1.00e+00 | 6.47e-12 | 1.00e+00 | 3.41e-14 | 9.91e-01 | 2.30e-08 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 9.84e-01 | 1.00e+00 | 9.99e-01 | 9.96e-01 | 5.74e-10 | 5.90e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.67e-18 | 9.70e-01 | 9.77e-01 | 1.17e-06 | 6.96e-11 | 5.55e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 9.66e-01 | 1.00e+00 | 4.40e-01 | 3.29e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 | 1.00e+00 | 7.95e-01 | 8.82e-07 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 9.91e-01 | 1.00e+00 | 9.96e-01 | 7.20e-17 | 1.33e-47 | 1.00e+00 | 3.05e-03 | 8.06e-05 | 8.41e-01 | 1.00e+00 | 6.64e-12 | 1.00e+00 | 6.63e-39 | 9.66e-01 | 1.91e-03 | 6.89e-01 | 1.00e+00 | 1.05e-27 | 1.00e+00 | 9.95e-01 | 9.97e-01 | 1.00e+00 | 1.41e-42 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 2.46e-07 | 4.24e-08 | 1.00e+00 | 9.66e-01 | 1.00e+00 | 5.58e-11 | 9.98e-01 | 1.00e+00 | 1.02e-04 | 4.75e-26 | 9.72e-01 | 1.00e+00 | 2.13e-02 | 1.00e+00 | 8.27e-01 | 1.00e+00 | 1.25e-10 | 9.66e-01 | 4.91e-01 | 2.28e-01 | 1.00e+00 | 1.69e-08 | 1.00e+00 | 6.04e-03 | 9.30e-03 | 1.00e+00 | 4.43e-05 | 1.00e+00 | 4.41e-08 | 9.42e-01 | 3.74e-03 | 1.66e-07 | 1.00e+00 | 2.59e-14 | 1.00e+00 | 3.74e-03 | 1.00e+00 | 8.39e-01 | 9.66e-01 | 1.00e+00 | 5.28e-03 | 9.81e-01 | 3.74e-03 | 4.70e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.85e-01 | 5.50e-01 | 9.80e-01 | 1.87e-03 | 1.00e+00 | 1.35e-55 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.23e-01 | 7.97e-01 | 0.00e+00 | 1.00e+00 | 9.66e-01 | 1.00e+00 | 9.66e-01 | 2.35e-23 | 6.21e-01 | 3.50e-28 | 7.77e-01 | 1.00e+00 | 1.00e+00 | 3.44e-13 | 1.00e+00 | 9.60e-01 | 1.00e+00 | 0.00e+00 | 9.99e-01 | 1.00e+00 | 8.89e-01 | 8.41e-02 | 1.10e-25 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.34e-09 | 3.18e-06 | 3.74e-03 | 1.54e-47 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 6.91e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.21e-01 | 9.93e-01 | 9.98e-01 | 6.84e-49 | 3.90e-07 | 9.99e-01 | 1.00e+00 | 1.17e-03 | 9.66e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.07e-07 | 1.00e+00 | 8.27e-01 | 8.27e-01 | 1.00e+00 | 9.99e-01 | 9.75e-01 | 1.27e-11 | 1.00e+00 | 9.66e-01 | 1.11e-06 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 2.32e-02 | 3.24e-19 | 1.00e+00 | 1.00e+00 | 1.49e-05 | 9.99e-01 | 1.00e+00 | 8.58e-01 | 1.00e+00 | 1.00e+00 | 8.27e-01 | 1.00e+00 | 1.00e+00 | 9.87e-01 | 1.00e+00 | 2.05e-01 | 9.74e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.95e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.78e-06 | 9.47e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.51e-02 | 1.97e-26 | 1.17e-24 | 1.00e+00 | 1.00e+00 | 9.66e-01 | 5.04e-18 | 8.21e-01 | 1.00e+00 | 8.66e-01 | 9.99e-01 | 2.39e-09 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 5.17e-04 | 0.00e+00 |
| 31 | 1.28e-15 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 8.77e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.79e-01 | 9.61e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.44e-03 | 4.52e-06 | 9.14e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.69e-03 | 1.00e+00 | 1.08e-22 | 1.00e+00 | 1.00e-33 | 1.00e+00 | 1.03e-04 | 1.00e+00 | 1.00e+00 | 9.03e-02 | 8.54e-01 | 1.00e+00 | 1.00e+00 | 9.82e-01 | 1.00e+00 | 3.04e-02 | 1.00e+00 | 2.24e-08 | 1.00e+00 | 1.00e+00 | 7.03e-01 | 1.00e+00 | 1.00e+00 | 1.78e-09 | 1.00e+00 | 2.02e-63 | 1.58e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.55e-13 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 2.45e-01 | 1.00e+00 | 3.21e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.23e-14 | 4.11e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.58e-16 | 1.00e+00 | 7.33e-01 | 1.44e-05 | 2.82e-04 | 9.18e-01 | 1.00e+00 | 1.42e-25 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.01e-01 | 2.62e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.03e-05 | 0.00e+00 | 1.00e+00 | 6.43e-06 | 1.00e+00 | 2.69e-03 | 2.47e-02 | 1.00e+00 | 3.85e-03 | 9.75e-01 | 9.11e-46 | 1.00e+00 | 9.83e-01 | 7.38e-09 | 1.00e+00 | 1.00e+00 | 2.76e-10 | 1.00e+00 | 1.21e-37 | 1.00e+00 | 8.79e-07 | 9.21e-01 | 1.00e+00 | 6.94e-26 | 1.00e+00 | 2.09e-11 | 9.99e-01 | 1.00e+00 | 3.21e-71 | 0.00e+00 | 9.92e-01 | 9.53e-01 | 3.06e-10 | 4.12e-08 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.23e-04 | 1.07e-20 | 1.00e+00 | 1.00e+00 | 1.76e-05 | 1.00e+00 | 4.24e-01 | 1.00e+00 | 4.05e-10 | 1.00e+00 | 6.53e-06 | 1.00e+00 | 1.00e+00 | 1.44e-08 | 1.00e+00 | 1.85e-01 | 1.40e-01 | 1.00e+00 | 7.84e-01 | 1.00e+00 | 1.26e-10 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.75e-01 | 1.69e-13 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.54e-01 | 3.29e-02 | 1.00e+00 | 1.00e+00 | 6.83e-07 | 1.00e+00 | 4.24e-01 | 1.00e+00 | 4.24e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 3.83e-01 | 3.35e-03 | 9.99e-01 | 2.42e-51 | 1.00e+00 | 1.00e+00 | 9.59e-01 | 1.00e+00 | 9.99e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.78e-23 | 5.04e-01 | 8.71e-27 | 3.57e-01 | 1.00e+00 | 1.00e+00 | 8.39e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 4.79e-04 | 1.00e+00 | 2.10e-01 | 1.95e-22 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 3.92e-13 | 1.12e-07 | 1.00e+00 | 8.77e-52 | 5.92e-02 | 1.00e+00 | 1.00e+00 | 3.59e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.56e-01 | 0.00e+00 | 1.00e+00 | 9.77e-48 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.45e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 9.85e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.60e-01 | 7.17e-15 | 1.00e+00 | 1.00e+00 | 6.51e-05 | 5.28e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 3.07e-18 | 1.00e+00 | 1.00e+00 | 5.62e-04 | 1.00e+00 | 1.00e+00 | 2.80e-02 | 1.00e+00 | 1.00e+00 | 4.24e-01 | 1.00e+00 | 1.00e+00 | 5.94e-01 | 1.00e+00 | 6.82e-04 | 6.09e-08 | 1.00e+00 | 5.72e-27 | 1.00e+00 | 9.64e-01 | 1.00e+00 | 7.28e-01 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.04e-05 | 2.84e-01 | 1.00e+00 | 1.00e+00 | 8.78e-01 | 1.00e+00 | 9.87e-01 | 1.62e-25 | 3.72e-17 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.58e-17 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 4.99e-05 | 4.24e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.38e-01 | 1.00e+00 | 0.00e+00 |
| 32 | 2.00e-15 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.88e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.93e-04 | 8.58e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 9.70e-04 | 1.06e-04 | 9.56e-01 | 3.82e-12 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.90e-01 | 5.10e-01 | 1.00e+00 | 6.99e-02 | 1.00e+00 | 1.83e-02 | 1.00e+00 | 1.26e-05 | 2.40e-01 | 1.00e+00 | 1.31e-01 | 9.99e-01 | 8.72e-10 | 1.00e+00 | 9.19e-01 | 1.00e+00 | 9.34e-01 | 3.05e-01 | 2.38e-08 | 1.00e+00 | 1.00e+00 | 8.03e-18 | 1.00e+00 | 1.00e+00 | 1.26e-08 | 1.00e+00 | 3.19e-62 | 4.04e-06 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.63e-18 | 1.00e+00 | 2.53e-10 | 1.00e+00 | 1.69e-14 | 1.00e+00 | 1.59e-09 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 5.05e-34 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 2.74e-09 | 2.37e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.31e-16 | 9.99e-01 | 9.97e-01 | 3.30e-02 | 2.74e-04 | 4.00e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.50e-02 | 1.95e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.10e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.25e-03 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.05e-03 | 1.00e+00 | 9.98e-01 | 5.67e-17 | 9.12e-45 | 1.00e+00 | 2.60e-02 | 6.38e-08 | 8.54e-01 | 1.00e+00 | 3.68e-14 | 1.00e+00 | 5.61e-81 | 1.00e+00 | 1.73e-03 | 8.92e-01 | 1.00e+00 | 2.46e-25 | 1.00e+00 | 1.42e-12 | 9.82e-01 | 1.00e+00 | 3.97e-38 | 1.00e+00 | 8.13e-01 | 9.96e-01 | 5.70e-09 | 6.99e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.83e-01 | 1.00e+00 | 5.70e-05 | 1.98e-21 | 8.79e-01 | 1.00e+00 | 5.49e-03 | 9.99e-01 | 4.86e-01 | 1.00e+00 | 5.94e-08 | 1.00e+00 | 4.62e-06 | 1.00e+00 | 1.00e+00 | 1.67e-08 | 1.00e+00 | 2.08e-03 | 1.77e-02 | 1.00e+00 | 1.04e-04 | 1.00e+00 | 2.64e-11 | 1.00e+00 | 8.89e-03 | 1.00e+00 | 8.57e-01 | 1.31e-13 | 1.00e+00 | 8.89e-03 | 1.00e+00 | 2.83e-04 | 1.00e+00 | 3.39e-01 | 1.52e-03 | 9.89e-01 | 8.89e-03 | 1.44e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.89e-03 | 9.93e-01 | 9.97e-01 | 9.99e-01 | 9.24e-01 | 2.15e-02 | 9.86e-01 | 6.93e-52 | 9.88e-01 | 1.00e+00 | 7.93e-01 | 1.20e-07 | 9.37e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 3.14e-01 | 1.29e-26 | 3.19e-01 | 1.00e+00 | 1.00e+00 | 2.38e-12 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.25e-01 | 2.74e-22 | 1.00e+00 | 1.00e+00 | 8.54e-07 | 7.10e-08 | 1.10e-09 | 8.89e-03 | 3.02e-39 | 4.19e-01 | 1.00e+00 | 1.00e+00 | 3.13e-01 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 3.09e-01 | 1.00e+00 | 1.00e+00 | 3.43e-47 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.11e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.01e-01 | 1.00e+00 | 1.93e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.70e-01 | 9.36e-01 | 5.45e-14 | 0.00e+00 | 1.00e+00 | 1.66e-06 | 1.23e-01 | 1.00e+00 | 1.00e+00 | 8.90e-01 | 9.03e-18 | 1.00e+00 | 4.54e-01 | 7.18e-05 | 9.99e-01 | 1.00e+00 | 9.49e-01 | 1.00e+00 | 1.00e+00 | 4.86e-01 | 0.00e+00 | 1.00e+00 | 1.66e-01 | 1.00e+00 | 1.76e-02 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 8.11e-01 | 1.00e+00 | 9.95e-01 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 4.91e-06 | 9.57e-01 | 1.00e+00 | 1.00e+00 | 2.36e-01 | 1.00e+00 | 7.27e-04 | 3.75e-25 | 3.27e-17 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.83e-16 | 9.92e-01 | 1.00e+00 | 7.70e-01 | 1.00e+00 | 4.44e-06 | 8.89e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.04e-01 | 1.11e-02 | 0.00e+00 |
| 33 | 4.29e-15 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 0.00e+00 | 6.75e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.14e-01 | 1.00e+00 | 1.00e+00 | 4.85e-05 | 9.83e-01 | 1.00e+00 | 9.99e-01 | 9.85e-01 | 1.00e+00 | 9.95e-01 | 1.00e+00 | 3.64e-02 | 3.74e-06 | 7.51e-01 | 1.97e-01 | 1.00e+00 | 9.98e-01 | 8.50e-01 | 3.61e-01 | 1.20e-04 | 1.00e+00 | 3.32e-02 | 1.00e+00 | 2.30e-02 | 1.00e+00 | 1.91e-06 | 2.74e-01 | 1.00e+00 | 4.12e-06 | 9.94e-01 | 2.74e-01 | 9.90e-01 | 9.26e-01 | 1.00e+00 | 2.36e-03 | 9.21e-01 | 3.14e-06 | 1.00e+00 | 1.00e+00 | 5.29e-01 | 1.00e+00 | 1.00e+00 | 2.06e-10 | 1.00e+00 | 4.01e-61 | 3.11e-03 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 6.15e-11 | 1.00e+00 | 5.81e-13 | 9.99e-01 | 1.53e-13 | 9.99e-01 | 2.71e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 2.69e-02 | 1.00e+00 | 9.94e-01 | 9.98e-01 | 1.00e+00 | 6.92e-01 | 1.47e-08 | 3.35e-01 | 1.00e+00 | 5.76e-01 | 1.00e+00 | 8.20e-01 | 1.00e+00 | 1.00e+00 | 6.26e-16 | 4.10e-01 | 9.80e-01 | 8.54e-07 | 1.65e-02 | 6.96e-01 | 9.57e-01 | 9.33e-01 | 1.00e+00 | 9.90e-01 | 1.00e+00 | 9.08e-01 | 3.16e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 7.37e-02 | 1.00e+00 | 9.90e-01 | 5.13e-09 | 0.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.00e-01 | 5.88e-02 | 9.08e-45 | 9.98e-01 | 7.89e-01 | 4.53e-09 | 8.22e-01 | 1.00e+00 | 9.50e-12 | 1.00e+00 | 2.82e-44 | 9.90e-01 | 4.85e-03 | 4.30e-01 | 1.00e+00 | 3.72e-25 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.34e-28 | 1.00e+00 | 9.92e-01 | 9.99e-01 | 7.91e-11 | 2.35e-08 | 1.32e-02 | 9.90e-01 | 9.29e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.54e-04 | 6.33e-20 | 1.00e+00 | 1.00e+00 | 3.39e-08 | 1.00e+00 | 5.48e-01 | 1.00e+00 | 8.24e-09 | 9.90e-01 | 1.32e-02 | 4.75e-23 | 1.32e-02 | 1.63e-07 | 1.00e+00 | 3.11e-01 | 3.64e-03 | 9.98e-01 | 5.52e-05 | 1.00e+00 | 6.65e-09 | 1.00e+00 | 1.32e-02 | 1.00e+00 | 9.99e-01 | 1.69e-03 | 1.00e+00 | 1.32e-02 | 1.00e+00 | 9.98e-01 | 1.32e-02 | 9.71e-01 | 6.72e-06 | 9.90e-01 | 1.32e-02 | 2.94e-06 | 1.00e+00 | 5.48e-01 | 9.97e-01 | 5.48e-01 | 9.99e-01 | 9.99e-01 | 9.97e-01 | 2.24e-01 | 1.07e-03 | 1.00e+00 | 3.38e-53 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 7.25e-01 | 0.00e+00 | 1.00e+00 | 9.90e-01 | 1.00e+00 | 1.32e-02 | 1.07e-21 | 4.62e-01 | 4.31e-02 | 2.59e-01 | 1.00e+00 | 1.00e+00 | 8.62e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 2.54e-04 | 1.00e+00 | 3.18e-03 | 1.41e-23 | 1.00e+00 | 9.98e-01 | 8.15e-02 | 1.02e-11 | 3.35e-05 | 1.32e-02 | 1.32e-41 | 8.36e-02 | 1.00e+00 | 1.00e+00 | 2.36e-01 | 7.49e-01 | 9.99e-01 | 1.00e+00 | 4.02e-02 | 0.00e+00 | 1.00e+00 | 1.66e-46 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.48e-02 | 1.32e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.94e-01 | 9.16e-01 | 3.02e-07 | 8.26e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 9.66e-01 | 2.94e-18 | 1.32e-02 | 9.90e-01 | 1.27e-05 | 9.12e-01 | 9.94e-01 | 1.00e+00 | 9.91e-01 | 2.15e-17 | 1.00e+00 | 1.00e+00 | 1.68e-04 | 9.78e-01 | 1.00e+00 | 8.32e-01 | 1.00e+00 | 1.00e+00 | 5.48e-01 | 1.32e-02 | 1.00e+00 | 5.54e-01 | 1.00e+00 | 1.51e-04 | 2.41e-11 | 1.32e-02 | 9.62e-01 | 9.98e-01 | 9.98e-01 | 3.95e-01 | 9.73e-04 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.43e-04 | 4.05e-05 | 1.00e+00 | 1.00e+00 | 8.25e-01 | 1.00e+00 | 4.85e-02 | 3.61e-24 | 4.17e-16 | 1.00e+00 | 1.32e-02 | 1.32e-02 | 6.33e-18 | 9.52e-01 | 1.00e+00 | 5.62e-01 | 1.00e+00 | 2.35e-05 | 5.48e-01 | 1.00e+00 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 2.85e-01 | 1.30e-02 | 0.00e+00 |
| 34 | 1.63e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.47e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.20e-01 | 1.00e+00 | 1.00e+00 | 8.75e-05 | 8.55e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.65e-04 | 1.72e-06 | 4.73e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.86e-01 | 2.89e-01 | 1.00e+00 | 1.88e-24 | 1.00e+00 | 2.41e-31 | 1.00e+00 | 1.15e-03 | 3.30e-01 | 1.00e+00 | 4.72e-04 | 9.99e-01 | 1.35e-01 | 1.00e+00 | 9.99e-01 | 7.68e-01 | 1.60e-03 | 1.00e+00 | 5.92e-08 | 9.97e-01 | 1.00e+00 | 6.57e-20 | 1.00e+00 | 1.00e+00 | 2.56e-11 | 9.93e-01 | 8.58e-60 | 1.85e-02 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.04e-08 | 1.00e+00 | 3.60e-11 | 1.00e+00 | 5.34e-16 | 9.98e-01 | 1.25e-10 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 6.08e-01 | 1.00e+00 | 7.97e-17 | 2.33e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 3.02e-16 | 6.66e-01 | 9.81e-01 | 2.66e-06 | 2.39e-04 | 6.86e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.28e-01 | 1.79e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 2.42e-06 | 0.00e+00 | 1.00e+00 | 4.81e-05 | 1.00e+00 | 9.97e-01 | 1.66e-02 | 1.00e+00 | 1.00e+00 | 9.09e-07 | 1.28e-43 | 1.00e+00 | 1.03e-02 | 2.28e-09 | 9.55e-01 | 1.00e+00 | 1.78e-02 | 1.00e+00 | 1.51e-36 | 1.00e+00 | 8.03e-05 | 9.84e-01 | 1.00e+00 | 2.99e-24 | 1.00e+00 | 2.93e-10 | 9.81e-01 | 1.00e+00 | 1.99e-40 | 6.08e-01 | 9.26e-01 | 1.00e+00 | 1.03e-09 | 4.64e-08 | 1.93e-02 | 1.00e+00 | 9.59e-01 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 8.43e-04 | 5.45e-20 | 9.94e-01 | 1.00e+00 | 1.17e-03 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 1.60e-08 | 1.00e+00 | 5.28e-06 | 1.85e-04 | 1.00e+00 | 2.32e-07 | 1.00e+00 | 3.35e-01 | 2.16e-02 | 1.00e+00 | 1.79e-04 | 1.00e+00 | 4.78e-11 | 1.00e+00 | 1.93e-02 | 1.00e+00 | 9.93e-01 | 2.22e-12 | 1.00e+00 | 1.93e-02 | 1.00e+00 | 1.00e+00 | 6.08e-01 | 8.78e-01 | 9.07e-02 | 1.00e+00 | 1.93e-02 | 2.77e-01 | 1.00e+00 | 1.93e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.52e-01 | 2.75e-01 | 3.30e-02 | 1.00e+00 | 1.96e-53 | 1.00e+00 | 1.00e+00 | 9.87e-01 | 1.00e+00 | 8.70e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 6.08e-01 | 1.06e-21 | 7.57e-01 | 7.89e-26 | 9.90e-01 | 1.00e+00 | 1.00e+00 | 1.30e-11 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.93e-01 | 9.92e-01 | 1.00e+00 | 4.88e-02 | 1.58e-23 | 1.00e+00 | 9.33e-01 | 9.91e-01 | 1.27e-08 | 3.32e-08 | 1.93e-02 | 2.72e-48 | 1.31e-01 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 8.94e-03 | 6.08e-01 | 1.00e+00 | 7.80e-45 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.93e-04 | 6.08e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.73e-01 | 9.96e-01 | 2.57e-10 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.83e-01 | 1.26e-10 | 1.00e+00 | 1.00e+00 | 1.74e-08 | 7.05e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 8.40e-17 | 1.00e+00 | 1.00e+00 | 3.36e-04 | 9.99e-01 | 1.00e+00 | 1.23e-01 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 1.00e+00 | 8.07e-01 | 1.00e+00 | 2.93e-03 | 7.36e-01 | 1.93e-02 | 1.00e+00 | 1.00e+00 | 9.89e-01 | 9.55e-01 | 9.54e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 2.50e-06 | 7.46e-01 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.69e-01 | 9.28e-24 | 3.41e-16 | 1.00e+00 | 1.00e+00 | 6.08e-01 | 3.83e-16 | 9.84e-01 | 1.00e+00 | 9.50e-01 | 9.93e-01 | 5.24e-06 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.97e-02 | 0.00e+00 |
| 35 | 4.47e-14 | 1.00e+00 | 9.75e-01 | 1.00e+00 | 0.00e+00 | 7.69e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.54e-01 | 1.00e+00 | 1.00e+00 | 1.30e-05 | 5.57e-01 | 1.00e+00 | 9.99e-01 | 9.82e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 5.93e-02 | 1.29e-05 | 8.38e-01 | 2.88e-01 | 1.00e+00 | 9.99e-01 | 7.79e-01 | 4.81e-01 | 3.43e-04 | 1.00e+00 | 6.24e-02 | 1.00e+00 | 4.51e-02 | 1.00e+00 | 7.51e-06 | 3.84e-01 | 1.00e+00 | 1.51e-05 | 9.95e-01 | 3.84e-01 | 9.59e-01 | 9.61e-01 | 1.00e+00 | 1.93e-03 | 8.32e-01 | 8.47e-08 | 1.00e+00 | 1.00e+00 | 6.48e-01 | 1.00e+00 | 1.00e+00 | 5.87e-11 | 1.00e+00 | 1.54e-58 | 1.43e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.03e-12 | 1.00e+00 | 4.92e-12 | 9.95e-01 | 1.31e-12 | 1.00e+00 | 1.19e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 5.18e-02 | 1.00e+00 | 9.98e-01 | 9.98e-01 | 1.00e+00 | 7.92e-01 | 2.34e-08 | 4.52e-01 | 1.00e+00 | 6.92e-01 | 1.00e+00 | 8.90e-01 | 1.00e+00 | 1.00e+00 | 7.06e-15 | 5.32e-01 | 9.91e-01 | 3.54e-06 | 2.85e-04 | 7.95e-01 | 9.79e-01 | 9.66e-01 | 1.00e+00 | 9.59e-01 | 1.00e+00 | 8.93e-02 | 4.48e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.26e-01 | 1.00e+00 | 9.96e-01 | 6.78e-09 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.90e-02 | 1.00e+00 | 9.45e-01 | 1.03e-01 | 1.03e-42 | 1.00e+00 | 8.65e-01 | 5.33e-10 | 8.92e-01 | 1.00e+00 | 2.35e-11 | 1.00e+00 | 3.09e-42 | 9.59e-01 | 8.05e-03 | 5.52e-01 | 1.00e+00 | 9.29e-24 | 1.00e+00 | 5.97e-10 | 1.00e+00 | 1.00e+00 | 1.89e-36 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.74e-10 | 2.60e-08 | 2.75e-02 | 9.59e-01 | 9.53e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.77e-03 | 1.02e-18 | 1.00e+00 | 1.00e+00 | 1.71e-07 | 1.00e+00 | 6.66e-01 | 1.00e+00 | 4.39e-08 | 9.59e-01 | 1.72e-07 | 9.97e-22 | 2.75e-02 | 1.16e-07 | 1.00e+00 | 4.08e-01 | 7.24e-03 | 1.00e+00 | 1.80e-04 | 1.00e+00 | 2.49e-11 | 1.00e+00 | 2.75e-02 | 1.00e+00 | 1.00e+00 | 1.86e-12 | 1.00e+00 | 2.75e-02 | 1.00e+00 | 9.93e-01 | 2.75e-02 | 9.87e-01 | 2.01e-05 | 9.96e-01 | 2.75e-02 | 1.15e-05 | 1.00e+00 | 6.66e-01 | 9.99e-01 | 6.66e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 3.20e-01 | 2.39e-03 | 1.00e+00 | 7.21e-51 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 7.58e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 2.75e-02 | 2.00e-20 | 5.84e-01 | 3.13e-24 | 3.67e-01 | 1.00e+00 | 1.00e+00 | 9.10e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 7.40e-04 | 1.00e+00 | 7.57e-03 | 2.14e-22 | 1.00e+00 | 9.99e-01 | 1.12e-01 | 5.21e-12 | 5.64e-07 | 2.75e-02 | 3.01e-41 | 1.40e-01 | 1.00e+00 | 1.00e+00 | 2.54e-01 | 8.37e-01 | 1.00e+00 | 1.00e+00 | 5.88e-02 | 0.00e+00 | 1.00e+00 | 2.16e-44 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.26e-02 | 2.75e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.55e-01 | 2.34e-07 | 8.95e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.84e-01 | 3.80e-17 | 2.75e-02 | 9.59e-01 | 2.85e-06 | 9.52e-01 | 9.98e-01 | 1.00e+00 | 9.96e-01 | 2.77e-16 | 1.00e+00 | 1.00e+00 | 4.30e-04 | 9.90e-01 | 1.00e+00 | 8.99e-01 | 1.00e+00 | 1.00e+00 | 6.66e-01 | 2.75e-02 | 1.00e+00 | 6.58e-01 | 1.00e+00 | 4.41e-05 | 6.46e-11 | 2.75e-02 | 9.82e-01 | 1.00e+00 | 9.99e-01 | 5.16e-01 | 2.56e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.63e-04 | 1.35e-04 | 1.00e+00 | 1.00e+00 | 8.85e-01 | 1.00e+00 | 8.74e-02 | 6.80e-23 | 4.78e-15 | 1.00e+00 | 2.75e-02 | 2.75e-02 | 1.64e-17 | 9.75e-01 | 1.00e+00 | 6.79e-01 | 1.00e+00 | 7.88e-05 | 6.66e-01 | 9.99e-01 | 9.59e-01 | 1.00e+00 | 1.00e+00 | 3.12e-01 | 2.71e-02 | 0.00e+00 |
| 36 | 1.39e-13 | 1.00e+00 | 9.83e-01 | 1.00e+00 | 0.00e+00 | 8.13e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.67e-01 | 1.00e+00 | 1.00e+00 | 2.27e-05 | 5.98e-01 | 1.00e+00 | 9.96e-01 | 9.88e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 6.81e-02 | 4.62e-06 | 8.55e-01 | 3.42e-01 | 1.00e+00 | 1.00e+00 | 8.22e-01 | 5.42e-01 | 3.77e-04 | 1.00e+00 | 8.30e-02 | 1.00e+00 | 6.12e-02 | 1.00e+00 | 1.46e-05 | 4.43e-01 | 1.00e+00 | 1.56e-05 | 7.53e-01 | 4.43e-01 | 9.71e-01 | 9.72e-01 | 1.00e+00 | 3.06e-03 | 8.68e-01 | 1.87e-07 | 1.00e+00 | 1.00e+00 | 7.03e-01 | 1.00e+00 | 1.00e+00 | 1.30e-10 | 1.00e+00 | 2.90e-57 | 2.51e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.43e-11 | 1.00e+00 | 1.39e-11 | 9.97e-01 | 2.33e-12 | 1.00e+00 | 1.23e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 6.98e-02 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 8.33e-01 | 4.22e-09 | 5.13e-01 | 1.00e+00 | 7.44e-01 | 1.00e+00 | 9.17e-01 | 1.00e+00 | 1.00e+00 | 2.29e-14 | 5.92e-01 | 7.57e-01 | 7.02e-06 | 4.75e-04 | 8.36e-01 | 9.86e-01 | 2.80e-25 | 1.00e+00 | 9.71e-01 | 1.00e+00 | 1.16e-01 | 4.83e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.60e-01 | 1.00e+00 | 9.97e-01 | 1.06e-08 | 0.00e+00 | 1.00e+00 | 9.86e-01 | 1.00e+00 | 1.00e+00 | 6.61e-02 | 1.00e+00 | 8.42e-03 | 1.33e-01 | 1.06e-41 | 1.00e+00 | 8.96e-01 | 1.13e-09 | 9.18e-01 | 1.00e+00 | 6.38e-11 | 1.00e+00 | 3.13e-41 | 9.71e-01 | 1.20e-02 | 6.12e-01 | 1.00e+00 | 2.47e-23 | 1.00e+00 | 1.50e-09 | 1.00e+00 | 1.00e+00 | 1.53e-35 | 1.00e+00 | 9.98e-01 | 9.48e-01 | 9.45e-11 | 4.59e-08 | 3.84e-02 | 9.71e-01 | 9.67e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.83e-03 | 3.95e-18 | 1.00e+00 | 1.00e+00 | 3.71e-07 | 1.00e+00 | 7.20e-01 | 1.00e+00 | 9.88e-08 | 9.71e-01 | 9.41e-08 | 4.41e-21 | 3.84e-02 | 2.41e-07 | 1.00e+00 | 4.39e-01 | 4.80e-03 | 1.00e+00 | 3.13e-04 | 1.00e+00 | 6.74e-11 | 1.00e+00 | 3.84e-02 | 1.00e+00 | 1.00e+00 | 5.35e-12 | 1.00e+00 | 3.84e-02 | 1.00e+00 | 9.96e-01 | 3.84e-02 | 9.91e-01 | 1.56e-05 | 9.98e-01 | 3.84e-02 | 2.21e-05 | 1.00e+00 | 7.20e-01 | 9.97e-01 | 7.20e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.24e-01 | 3.75e-03 | 1.00e+00 | 1.02e-49 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.03e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 3.84e-02 | 8.38e-20 | 6.43e-01 | 1.54e-23 | 4.26e-01 | 1.00e+00 | 1.00e+00 | 9.33e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.22e-03 | 1.00e+00 | 1.13e-02 | 9.00e-22 | 1.00e+00 | 1.00e+00 | 1.43e-01 | 2.88e-12 | 9.49e-07 | 3.84e-02 | 2.93e-40 | 1.77e-01 | 1.00e+00 | 1.00e+00 | 3.05e-01 | 8.72e-01 | 1.00e+00 | 1.00e+00 | 7.31e-02 | 0.00e+00 | 1.00e+00 | 2.37e-43 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.70e-02 | 3.84e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.68e-01 | 7.86e-08 | 9.21e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.89e-01 | 1.37e-16 | 3.84e-02 | 9.71e-01 | 4.92e-06 | 9.66e-01 | 9.97e-01 | 1.00e+00 | 9.98e-01 | 9.62e-16 | 1.00e+00 | 1.00e+00 | 7.26e-04 | 8.99e-01 | 1.00e+00 | 9.24e-01 | 1.00e+00 | 1.00e+00 | 7.20e-01 | 3.84e-02 | 1.00e+00 | 7.12e-01 | 1.00e+00 | 7.55e-05 | 1.71e-10 | 3.84e-02 | 1.74e-23 | 1.00e+00 | 1.00e+00 | 5.76e-01 | 4.01e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.60e-04 | 2.37e-04 | 1.00e+00 | 1.00e+00 | 8.95e-01 | 1.00e+00 | 1.14e-01 | 3.16e-22 | 1.56e-14 | 1.00e+00 | 3.84e-02 | 3.84e-02 | 6.00e-17 | 9.83e-01 | 1.00e+00 | 7.32e-01 | 1.00e+00 | 1.41e-04 | 7.20e-01 | 9.99e-01 | 9.71e-01 | 1.00e+00 | 1.00e+00 | 3.68e-01 | 3.78e-02 | 0.00e+00 |
| 37 | 9.29e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 7.39e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 8.46e-04 | 1.15e-02 | 1.00e+00 | 9.93e-01 | 9.97e-01 | 1.00e+00 | 8.00e-01 | 1.00e+00 | 5.43e-05 | 2.95e-07 | 6.97e-01 | 3.50e-10 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.56e-01 | 4.67e-03 | 1.00e+00 | 1.08e-01 | 1.00e+00 | 8.67e-02 | 1.00e+00 | 5.52e-04 | 5.03e-01 | 1.00e+00 | 1.58e-03 | 3.07e-03 | 2.88e-08 | 1.00e+00 | 8.39e-01 | 1.00e+00 | 2.21e-02 | 9.87e-01 | 1.66e-13 | 9.86e-01 | 1.00e+00 | 2.59e-15 | 1.00e+00 | 1.00e+00 | 6.98e-08 | 1.00e+00 | 5.35e-56 | 9.47e-07 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.94e-11 | 1.00e+00 | 1.34e-08 | 1.00e+00 | 4.61e-13 | 9.98e-01 | 5.73e-11 | 1.00e+00 | 9.99e-01 | 0.00e+00 | 3.23e-30 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.00e+00 | 1.00e+00 | 5.41e-08 | 1.76e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.68e-14 | 9.85e-01 | 9.97e-01 | 1.35e-05 | 1.85e-11 | 5.94e-01 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.11e-01 | 4.57e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.54e-01 | 1.45e-01 | 1.00e+00 | 9.99e-01 | 1.05e-04 | 0.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 3.48e-05 | 3.21e-02 | 1.00e+00 | 9.81e-01 | 1.90e-14 | 3.17e-40 | 1.00e+00 | 5.54e-02 | 1.78e-09 | 9.39e-01 | 1.00e+00 | 1.83e-01 | 1.00e+00 | 1.88e-73 | 1.00e+00 | 2.62e-01 | 1.18e-01 | 1.00e+00 | 2.12e-22 | 1.00e+00 | 1.59e-09 | 9.99e-01 | 1.00e+00 | 2.34e-37 | 7.69e-01 | 9.97e-01 | 9.99e-01 | 2.84e-09 | 2.93e-08 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.93e-07 | 9.70e-01 | 1.00e+00 | 4.84e-04 | 1.37e-24 | 9.21e-01 | 1.00e+00 | 1.31e-07 | 9.99e-01 | 7.69e-01 | 1.00e+00 | 4.97e-07 | 1.00e+00 | 3.57e-04 | 1.00e+00 | 1.00e+00 | 1.94e-08 | 1.00e+00 | 4.15e-02 | 4.35e-02 | 1.00e+00 | 6.87e-04 | 1.00e+00 | 1.19e-09 | 1.00e+00 | 5.26e-02 | 9.95e-01 | 8.71e-01 | 1.84e-11 | 1.00e+00 | 5.26e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.94e-01 | 2.21e-04 | 9.92e-01 | 5.26e-02 | 4.14e-05 | 1.00e+00 | 7.69e-01 | 1.00e+00 | 7.69e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 8.90e-01 | 2.89e-08 | 9.89e-01 | 1.00e-47 | 1.00e+00 | 1.00e+00 | 8.03e-01 | 1.00e+00 | 9.76e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 8.90e-22 | 3.18e-01 | 1.10e-23 | 5.20e-01 | 2.72e-26 | 1.00e+00 | 1.30e-10 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.98e-03 | 1.00e+00 | 4.14e-02 | 5.52e-23 | 1.00e+00 | 1.00e+00 | 3.12e-04 | 3.57e-13 | 4.38e-10 | 5.26e-02 | 1.08e-39 | 4.27e-04 | 1.00e+00 | 1.00e+00 | 5.68e-01 | 9.94e-01 | 1.00e+00 | 1.00e+00 | 7.22e-01 | 0.00e+00 | 1.00e+00 | 1.43e-42 | 1.28e-07 | 1.00e+00 | 1.00e+00 | 3.17e-03 | 1.00e+00 | 3.88e-26 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.09e-01 | 1.00e+00 | 2.29e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.72e-01 | 6.48e-19 | 0.00e+00 | 1.00e+00 | 2.52e-05 | 8.06e-02 | 1.00e+00 | 1.00e+00 | 3.02e-03 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.70e-03 | 9.93e-01 | 1.00e+00 | 9.34e-01 | 1.00e+00 | 1.00e+00 | 7.69e-01 | 0.00e+00 | 1.00e+00 | 1.02e-01 | 1.00e+00 | 4.84e-03 | 4.31e-09 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 8.22e-01 | 1.00e+00 | 2.97e-03 | 9.87e-01 | 9.99e-01 | 1.00e+00 | 5.96e-05 | 3.64e-06 | 1.00e+00 | 1.00e+00 | 9.61e-01 | 1.00e+00 | 2.24e-01 | 2.17e-22 | 2.00e-20 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.02e-15 | 9.88e-01 | 1.00e+00 | 8.81e-01 | 1.00e+00 | 4.03e-05 | 7.69e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.49e-01 | 5.18e-02 | 0.00e+00 |
| 38 | 4.55e-14 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 9.09e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.56e-01 | 1.00e+00 | 1.00e+00 | 4.85e-06 | 2.50e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 3.88e-02 | 9.75e-05 | 8.08e-01 | 1.61e-11 | 0.00e+00 | 9.98e-01 | 1.00e+00 | 9.53e-01 | 5.35e-02 | 1.00e+00 | 2.06e-01 | 1.00e+00 | 9.89e-02 | 1.00e+00 | 1.82e-03 | 5.59e-01 | 1.00e+00 | 1.88e-02 | 8.52e-01 | 3.40e-12 | 1.00e+00 | 9.79e-01 | 2.10e-01 | 9.53e-03 | 9.82e-01 | 2.47e-04 | 1.00e+00 | 1.00e+00 | 6.01e-15 | 1.00e+00 | 1.00e+00 | 1.82e-06 | 1.00e+00 | 8.30e-63 | 9.44e-03 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.73e-09 | 1.00e+00 | 3.61e-08 | 1.00e+00 | 1.11e-13 | 9.99e-01 | 9.88e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.29e-28 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.05e-02 | 9.96e-01 | 2.12e-15 | 4.39e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.35e-15 | 5.16e-04 | 8.59e-01 | 4.43e-05 | 1.50e-03 | 4.44e-01 | 1.00e+00 | 5.48e-21 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.80e-01 | 2.25e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.21e-01 | 1.00e+00 | 9.96e-01 | 5.29e-06 | 0.00e+00 | 1.00e+00 | 8.42e-05 | 1.00e+00 | 9.45e-01 | 3.14e-04 | 1.00e+00 | 1.38e-02 | 2.11e-14 | 2.57e-39 | 9.99e-01 | 5.68e-03 | 9.17e-08 | 9.50e-01 | 1.00e+00 | 1.19e-08 | 1.00e+00 | 4.37e-71 | 1.00e+00 | 1.55e-07 | 9.96e-01 | 1.00e+00 | 8.91e-25 | 1.00e+00 | 3.07e-09 | 1.00e+00 | 1.00e+00 | 1.46e-39 | 7.05e-02 | 1.00e+00 | 9.48e-01 | 5.86e-06 | 8.19e-08 | 0.00e+00 | 1.00e+00 | 3.23e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.30e-04 | 7.26e-16 | 4.29e-01 | 1.00e+00 | 3.85e-15 | 1.00e+00 | 8.13e-01 | 1.00e+00 | 3.31e-10 | 1.00e+00 | 1.47e-03 | 1.00e+00 | 1.00e+00 | 3.23e-06 | 1.00e+00 | 6.62e-04 | 5.37e-02 | 9.99e-01 | 2.50e-03 | 1.00e+00 | 1.42e-06 | 1.00e+00 | 7.05e-02 | 2.35e-13 | 9.99e-01 | 7.29e-11 | 9.99e-01 | 7.05e-02 | 1.00e+00 | 9.91e-01 | 1.00e+00 | 9.63e-01 | 8.74e-05 | 9.94e-01 | 7.05e-02 | 7.58e-05 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 9.99e-01 | 9.99e-01 | 1.00e+00 | 7.18e-01 | 4.21e-01 | 4.97e-02 | 1.00e+00 | 8.74e-49 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 7.09e-01 | 9.71e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 3.08e-19 | 8.90e-01 | 7.85e-24 | 7.27e-01 | 1.00e+00 | 1.00e+00 | 3.32e-10 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 9.41e-03 | 1.00e+00 | 1.93e-07 | 1.29e-25 | 1.00e+00 | 1.00e+00 | 5.36e-05 | 4.93e-11 | 1.28e-08 | 7.05e-02 | 4.57e-42 | 1.06e-01 | 1.00e+00 | 1.00e+00 | 6.67e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.01e-01 | 0.00e+00 | 1.00e+00 | 3.85e-42 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.30e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.43e-01 | 1.65e-06 | 1.00e+00 | 7.05e-02 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 9.78e-01 | 7.62e-22 | 0.00e+00 | 1.00e+00 | 5.82e-06 | 8.76e-01 | 4.29e-01 | 1.00e+00 | 1.00e+00 | 6.77e-15 | 1.00e+00 | 1.00e+00 | 8.27e-05 | 1.00e+00 | 1.00e+00 | 9.61e-01 | 1.00e+00 | 1.00e+00 | 8.13e-01 | 0.00e+00 | 1.00e+00 | 3.96e-01 | 1.00e+00 | 1.82e-01 | 3.34e-08 | 0.00e+00 | 2.09e-26 | 9.99e-01 | 9.98e-01 | 1.00e+00 | 5.94e-03 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.18e-07 | 1.23e-05 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 3.13e-01 | 7.48e-22 | 7.54e-14 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 5.80e-14 | 9.40e-01 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 2.28e-05 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.35e-01 | 6.73e-02 | 0.00e+00 |
| 39 | 6.31e-12 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 8.29e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.14e-01 | 1.00e+00 | 1.00e+00 | 9.71e-06 | 1.45e-01 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 4.09e-03 | 1.58e-06 | 6.87e-01 | 3.37e-09 | 0.00e+00 | 1.00e+00 | 6.84e-01 | 9.50e-01 | 2.07e-02 | 1.00e+00 | 1.02e-01 | 1.00e+00 | 1.49e-01 | 1.00e+00 | 6.49e-04 | 6.30e-01 | 1.00e+00 | 8.42e-04 | 1.00e+00 | 6.13e-08 | 1.00e+00 | 9.32e-01 | 1.00e+00 | 7.78e-01 | 1.00e+00 | 1.08e-06 | 1.00e+00 | 1.00e+00 | 2.20e-15 | 1.00e+00 | 1.00e+00 | 3.49e-12 | 1.00e+00 | 2.02e-61 | 1.46e-06 | 0.00e+00 | 8.51e-01 | 1.00e+00 | 1.00e+00 | 7.57e-11 | 1.00e+00 | 2.82e-08 | 1.00e+00 | 2.23e-12 | 1.00e+00 | 2.07e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 2.86e-28 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 1.72e-07 | 1.55e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.16e-15 | 9.47e-03 | 1.00e+00 | 4.48e-02 | 2.97e-04 | 8.34e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 3.95e-02 | 8.66e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.42e-01 | 1.00e+00 | 9.98e-01 | 5.10e-06 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 3.48e-02 | 3.12e-04 | 1.00e+00 | 1.00e+00 | 6.31e-14 | 1.98e-38 | 1.00e+00 | 1.06e-01 | 1.93e-09 | 9.77e-01 | 1.00e+00 | 3.98e-09 | 1.00e+00 | 5.26e-70 | 9.99e-01 | 5.08e-08 | 8.83e-01 | 1.00e+00 | 7.84e-21 | 1.00e+00 | 1.55e-08 | 9.98e-01 | 1.00e+00 | 1.05e-64 | 0.00e+00 | 9.86e-01 | 1.00e+00 | 2.36e-09 | 2.64e-04 | 0.00e+00 | 9.99e-01 | 2.38e-01 | 1.00e+00 | 9.99e-01 | 9.64e-01 | 4.79e-04 | 3.67e-17 | 4.47e-01 | 1.00e+00 | 1.51e-04 | 1.00e+00 | 8.51e-01 | 1.00e+00 | 1.80e-10 | 9.99e-01 | 7.91e-04 | 7.37e-01 | 1.00e+00 | 6.50e-08 | 1.00e+00 | 2.96e-02 | 4.92e-02 | 1.00e+00 | 2.72e-03 | 8.51e-01 | 8.18e-10 | 1.00e+00 | 9.28e-02 | 2.05e-13 | 9.85e-01 | 2.01e-10 | 1.00e+00 | 9.28e-02 | 1.00e+00 | 1.00e+00 | 9.99e-01 | 7.44e-01 | 8.99e-05 | 1.00e+00 | 9.28e-02 | 1.36e-04 | 1.00e+00 | 9.28e-02 | 1.00e+00 | 9.28e-02 | 1.51e-01 | 1.00e+00 | 1.00e+00 | 9.91e-01 | 1.16e-03 | 9.99e-01 | 8.91e-45 | 9.99e-01 | 1.00e+00 | 9.72e-01 | 1.00e+00 | 9.19e-01 | 0.00e+00 | 9.99e-01 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 7.80e-01 | 7.99e-23 | 7.70e-01 | 1.00e+00 | 1.00e+00 | 2.01e-09 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 0.00e+00 | 8.51e-01 | 1.28e-02 | 1.00e+00 | 1.13e-06 | 1.14e-21 | 1.00e+00 | 9.99e-01 | 1.17e-04 | 1.15e-10 | 5.15e-09 | 9.28e-02 | 3.50e-36 | 1.50e-02 | 1.00e+00 | 1.00e+00 | 6.81e-01 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 3.02e-01 | 0.00e+00 | 1.00e+00 | 1.03e-39 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 2.05e-03 | 9.99e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.95e-01 | 3.09e-01 | 4.04e-05 | 1.00e+00 | 9.28e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.96e-01 | 9.24e-20 | 0.00e+00 | 9.99e-01 | 2.21e-06 | 4.60e-01 | 5.45e-01 | 1.00e+00 | 9.91e-01 | 4.18e-14 | 1.00e+00 | 1.00e+00 | 3.69e-05 | 1.00e+00 | 1.00e+00 | 9.62e-01 | 1.00e+00 | 1.00e+00 | 8.51e-01 | 0.00e+00 | 1.00e+00 | 3.17e-01 | 1.00e+00 | 8.42e-05 | 5.67e-08 | 0.00e+00 | 9.99e-01 | 1.00e+00 | 9.76e-01 | 1.00e+00 | 5.90e-03 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 2.94e-05 | 2.86e-04 | 1.00e+00 | 1.00e+00 | 6.16e-01 | 1.00e+00 | 1.06e-01 | 3.04e-23 | 1.15e-13 | 1.00e+00 | 0.00e+00 | 9.99e-01 | 1.46e-13 | 9.97e-01 | 1.00e+00 | 9.85e-01 | 9.99e-01 | 4.63e-12 | 9.28e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 5.47e-01 | 9.88e-02 | 0.00e+00 |
| 40 | 4.29e-15 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 6.76e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.92e-03 | 1.23e-02 | 1.00e+00 | 1.00e+00 | 9.93e-01 | 1.00e+00 | 9.81e-01 | 1.00e+00 | 2.48e-03 | 2.08e-04 | 3.79e-01 | 4.24e-09 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.73e-01 | 1.57e-03 | 1.00e+00 | 3.79e-17 | 3.92e-04 | 1.10e-27 | 9.99e-01 | 6.19e-05 | 6.49e-01 | 1.00e+00 | 1.65e-02 | 1.16e-04 | 2.30e-07 | 1.00e+00 | 6.88e-01 | 1.00e+00 | 5.75e-02 | 1.00e+00 | 3.60e-05 | 9.99e-01 | 9.69e-01 | 2.40e-14 | 9.97e-01 | 1.00e+00 | 3.78e-09 | 1.00e+00 | 4.82e-60 | 2.39e-04 | 0.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.16e-16 | 1.20e-01 | 3.78e-08 | 1.00e+00 | 1.02e-11 | 9.98e-01 | 1.16e-11 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 8.03e-28 | 1.00e+00 | 9.47e-01 | 9.96e-01 | 1.00e+00 | 1.00e+00 | 2.31e-07 | 6.36e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 8.85e-13 | 9.99e-01 | 9.95e-01 | 1.48e-04 | 8.93e-04 | 7.96e-01 | 1.00e+00 | 9.98e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.97e-01 | 1.51e-02 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.92e-01 | 3.91e-01 | 1.00e+00 | 1.00e+00 | 8.04e-07 | 0.00e+00 | 1.00e+00 | 9.99e-01 | 1.00e+00 | 2.13e-01 | 8.04e-02 | 1.00e+00 | 9.69e-01 | 1.98e-15 | 2.57e-37 | 1.00e+00 | 2.00e-01 | 2.75e-05 | 9.28e-01 | 1.00e+00 | 9.81e-10 | 1.00e+00 | 8.06e-69 | 1.00e+00 | 5.47e-04 | 3.38e-02 | 1.00e+00 | 8.58e-21 | 1.00e+00 | 1.63e-11 | 9.86e-01 | 1.00e+00 | 1.13e-31 | 1.00e+00 | 7.04e-01 | 9.98e-01 | 6.61e-11 | 2.82e-07 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.88e-01 | 1.00e+00 | 1.00e+00 | 2.06e-04 | 7.77e-23 | 4.76e-01 | 1.00e+00 | 9.85e-07 | 1.00e+00 | 8.83e-01 | 1.00e+00 | 4.60e-06 | 1.00e+00 | 3.03e-06 | 1.00e+00 | 1.00e+00 | 1.98e-08 | 1.00e+00 | 4.53e-02 | 1.89e-01 | 1.00e+00 | 1.64e-03 | 1.00e+00 | 3.82e-06 | 9.93e-01 | 1.20e-01 | 1.15e-12 | 9.44e-01 | 7.18e-18 | 8.83e-01 | 1.20e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 4.63e-01 | 2.48e-04 | 9.90e-01 | 1.20e-01 | 6.19e-77 | 1.00e+00 | 8.83e-01 | 9.96e-01 | 1.20e-01 | 2.79e-01 | 9.99e-01 | 1.00e+00 | 8.36e-01 | 3.75e-03 | 9.96e-01 | 1.55e-43 | 9.96e-01 | 1.00e+00 | 9.04e-01 | 1.00e+00 | 6.74e-01 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 9.99e-01 | 1.00e+00 | 1.08e-19 | 4.78e-01 | 4.13e-23 | 3.79e-03 | 4.13e-24 | 1.00e+00 | 3.55e-09 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 1.30e-02 | 5.78e-19 | 1.00e+00 | 1.00e+00 | 4.40e-05 | 4.09e-12 | 1.27e-09 | 1.20e-01 | 1.14e-40 | 1.92e-03 | 1.00e+00 | 1.00e+00 | 9.70e-01 | 1.00e+00 | 9.96e-01 | 1.00e+00 | 3.44e-01 | 0.00e+00 | 1.00e+00 | 9.50e-42 | 9.96e-01 | 1.00e+00 | 9.99e-01 | 9.22e-03 | 1.00e+00 | 1.46e-31 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 7.28e-01 | 1.00e+00 | 2.56e-06 | 1.00e+00 | 1.20e-01 | 1.00e+00 | 1.00e+00 | 9.98e-01 | 8.43e-01 | 6.91e-20 | 0.00e+00 | 1.00e+00 | 8.60e-05 | 1.70e-01 | 1.00e+00 | 1.00e+00 | 7.47e-01 | 0.00e+00 | 1.00e+00 | 1.05e-01 | 3.40e-03 | 9.99e-01 | 1.00e+00 | 1.56e-01 | 1.00e+00 | 1.00e+00 | 1.20e-01 | 0.00e+00 | 1.00e+00 | 1.36e-01 | 1.00e+00 | 1.87e-02 | 4.16e-08 | 1.00e+00 | 9.94e-01 | 1.00e+00 | 9.16e-01 | 1.00e+00 | 7.28e-03 | 1.00e+00 | 1.39e-02 | 1.00e+00 | 2.63e-06 | 1.83e-04 | 1.00e+00 | 1.00e+00 | 5.81e-01 | 1.00e+00 | 5.52e-02 | 1.88e-20 | 4.52e-15 | 1.00e+00 | 0.00e+00 | 1.00e+00 | 2.16e-13 | 7.88e-01 | 1.00e+00 | 9.74e-01 | 1.00e+00 | 2.38e-05 | 1.20e-01 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 | 6.36e-01 | 9.30e-02 | 0.00e+00 |
Note that already at depth 2 we are getting some p-values of 0, indicating that overfitting is occurring for sparse labels (e.g. Construction & Landscaping at k=8). The largest value of k that avoids this overfitting is k = 6.
From the preceding section using elbow inspection and the gap statistic, we select dataset 10 (venue vector globally scaled to 1.4 range, census and venue density data scaled to 1.0 range), and k=8 as gap statistic-optimal model inputs (this allows feature Arts & Entertainment to be used at a decent significance level, as well as College & University at a marginal significance level).
We re-run the clustering a final time with only the most useful features at depth 0 and k=8:
k=8
featureset = 10
name = f'Curated Features, k={k}'
features = k_means_data[featureset]['features'].drop(columns=['Professional & Other Places','Residence','Travel & Transport'])
labels = k_means_data[featureset]['labels']
kmeans_model = KMeans(n_clusters=k, random_state=kmeans_params['random_state'], n_init=kmeans_params['n_init'])
kmeans_model.fit(features)
clusters = kmeans_model.labels_
We can see from the p-values that the significance is as expected.
df_eval_fs = pd.DataFrame(index=[k],columns=features.columns)
_, df_eval_fs.loc[k,:] = sklearn.feature_selection.f_classif(features,clusters)
display(df_eval_fs.style.applymap(highlight_below_plim).applymap(highlight_zero_red).format("{:.2e}"))
| Arts & Entertainment | College & University | Food | Nightlife Spot | Outdoors & Recreation | Shop & Service | Dwelling Density | Log10 (Population Density) | Log10 (1 - Residentiality) | Log10 (Venue Density) | |
|---|---|---|---|---|---|---|---|---|---|---|
| 8 | 3.69e-14 | 8.95e-01 | 1.53e-33 | 2.88e-09 | 3.32e-22 | 3.41e-19 | 1.44e-16 | 8.87e-13 | 3.91e-10 | 3.02e-26 |
We now display the means of each feature for each cluster, to gain some insight into what the clusters mean.
df_features = features.copy()
df_features['Postal Code'] = labels
df_features['Cluster'] = clusters
df_avg = df_features.groupby('Cluster').mean()
df_std = df_features.groupby('Cluster').std()
cm = sns.light_palette("green", as_cmap=True)
s = df_avg.style.background_gradient(cmap=cm).format("{:<0.2f}")
s
| Arts & Entertainment | College & University | Food | Nightlife Spot | Outdoors & Recreation | Shop & Service | Dwelling Density | Log10 (Population Density) | Log10 (1 - Residentiality) | Log10 (Venue Density) | |
|---|---|---|---|---|---|---|---|---|---|---|
| Cluster | ||||||||||
| 0 | 0.05 | 0.00 | 0.69 | 0.03 | 0.22 | 0.30 | 0.40 | 0.41 | 0.34 | 0.44 |
| 1 | 0.03 | 0.00 | 0.34 | 0.01 | 0.06 | 0.93 | 0.38 | 0.35 | 0.46 | 0.69 |
| 2 | 0.07 | 0.00 | 0.80 | 0.11 | 0.10 | 0.26 | 0.12 | 0.66 | 0.70 | 0.89 |
| 3 | 0.00 | 0.00 | 0.00 | 0.00 | 1.28 | 0.00 | 0.70 | 0.29 | 0.41 | 0.10 |
| 4 | 0.01 | 0.00 | 0.88 | 0.02 | 0.12 | 0.32 | 0.49 | 0.25 | 0.44 | 0.37 |
| 5 | 0.02 | 0.00 | 0.79 | 0.09 | 0.18 | 0.28 | 0.25 | 0.50 | 0.51 | 0.66 |
| 6 | 0.01 | 0.01 | 0.58 | 0.02 | 0.23 | 0.46 | 0.42 | 0.31 | 0.45 | 0.39 |
| 7 | 0.32 | 0.00 | 0.18 | 0.00 | 0.52 | 0.39 | 0.42 | 0.24 | 0.50 | 0.29 |
We can assign some descriptions to the clusters based on their categories above and knowledge of city structure:
We use Folium to display the clusters on a map.
import folium
import matplotlib.cm as cm
import matplotlib.colors
from folium.features import DivIcon
#centroidlatlon = [df_final['Latitude'].mean(), df_final['Longitude'].mean()]
map_toronto = folium.Map(location=centroidlatlon, zoom_start=11) # generate map centred around the center of postal codes
cluster_names = ['0. Mid-Urban Well-Rounded',
'1. Suburban Shop-Focused',
'2. Downtown',
'3. Suburban Family & Outdoors',
'4. Suburban Food-Focused',
'5. Urban',
'6. Suburban Well-Rounded',
'7. Suburban Arts & Outdoors']
feature_groups = [x for x in range(len(cluster_names))]
for k in range(len(feature_groups)):
feature_groups[k] = folium.FeatureGroup(name=cluster_names[k])
colors = [matplotlib.colors.to_hex(cm.hsv(x/(len(np.unique(clusters))))) for x in range(len(np.unique(clusters)))]
for (cluster, label) in zip(clusters,labels):
folium.CircleMarker(
(df_final.loc[df_final['Postal Code']==label,'Latitude'], df_final.loc[df_final['Postal Code']==label,'Longitude']),
radius=7,
color=colors[cluster],
fill = True,
fill_color = colors[cluster],
fill_opacity = 0.6,
popup=f"{label}, Cluster {cluster}",
tooltip=f"{label}, Cluster {cluster}"
).add_to(feature_groups[cluster])
folium.map.Marker(
(df_final.loc[df_final['Postal Code']==label,'Latitude'], df_final.loc[df_final['Postal Code']==label,'Longitude']),
icon=DivIcon(
icon_size=(150,36),
icon_anchor=(4,10),
html='<div style="font-size: 10pt">%s</div>' % str(cluster),
)
).add_to(feature_groups[cluster])
for k in range(len(feature_groups)):
map_toronto.add_child(feature_groups[k])
map_toronto.add_child(folium.map.LayerControl(position='bottomright',collapsed=False))
map_toronto
We segmented the postal codes of Toronto into clusters using a k-means model acting on features derived from population and dwelling informatino from the 2016 census and nearby venues obtained from Foursquare. The optimal number of clusters was found to be ~5-10, and the clustering explored for the specific case of 9 clusters. The resulting map and cluster descriptions are reasonable.
For future reference and improvement, some potential issues with the analysis were noted. The Foursquare venue data might be spotty, and the magnitude of selection bias in the dataset is unknown - not all venues appear in the dataset, and it is likely that certain locations and types of establishments are preferenced by individuals who use Foursquare. For example, not all universities and colleges appear in the venue data, and a zoo had a large number of exhibits that give it an outsized weight. The FSA areas seem relatively large, and an approach using a finer grid of locations and smaller search radius might yield improvement. Alternative datasets might be more thorough, such as telephone directories (organized by business type), or provide other quality data (such as census population profiles with age and ethnicity categories like here where reporting at FSA level is an option). Alternatively, a local venue database could be constructed by grid query of foursquare, and venue vectors constructed based on proximity. Restricting venue data to zero category depth is also suboptimal - in the future higher category depths should be explored with concurrent feature elimination (e.g. eliminating features appearing in fewer than an arbitrary number of locations, or below an arbitrary threshold global count).
Next we will parse the webpage using Beautiful Soup.
from bs4 import BeautifulSoup # had to install to environment in Anaconda
soup = BeautifulSoup(webpage.text, 'html.parser')
To view the HTML directly we could run:
print(soup.prettify())
But we can also inspect the webpage for the table we expect:
tables = soup.find_all('table') len(tables)
for i, table in enumerate(tables): print('Length of table {} string: {}, attributes: {}'.format(i,len(table.text),table.attrs))
import lxml import html5lib
str(tables[0])
df_pcodes = pd.read_html(str(tables[0]))[0]
type(df_pcodes) df_pcodes
conda install -c conda-forge geocoder
Gave problem with openssl-1.1.1h-he774522_0.tar.bz2
From Anaconda Navigator, I removed openssl, restarted, then installed it again.
Doing so seemed to reset the environment; I had to reinstall jupyterlab, pandas, numpy, lxml, html5lib, bs4.
I also discovered the Channels setting, and by adding conda-forge I was able to access packages that I installed through the prompt before: geocoder, jupyterlab-git, ipywidgets.
So there may be additional fallout from this, but at least I can use Aanconda as intended now that I can get the packages I need through the UI.
Unfortunately, geocoder.google('Ottawa, ON') and other services from geocoder failed to return data (over 200 explicit calls and over 20 minutes in a loop in the case of google, except for CanadaPost which did work, but that only returns a postal code). Instead I elect to use Nominatim, we'll see if that works for Toronto...
The following cells are the original geocoding work:
I would like to try to get latitude and longitude for each neighborhood, so let's make a new dataframe:
df_n = pd.DataFrame(columns=df_cleaned.columns)
for i, row in df_cleaned.iterrows():
for neighborhood in row['Neighbourhood'].split(', '):
df_n = df_n.append(row, ignore_index=True)
df_n.loc[df_n.shape[0]-1,'Neighbourhood'] = neighborhood
df_n.head(10)
When a neighborhood appears in several postal codes, part of the neighborhood appears in each one. In absence of geographic boundary polygons, let's weight the location of each neighborhood by the inverse of its number of appearances overall when we average the latitude and longitude of neighborhoods to get the postal code locations. So let's count the number of times the neighborhood appears in the list.
for i, row in df_n.iterrows():
df_n.loc[i, 'Count'] = int(df_n[df_n['Neighbourhood']==row['Neighbourhood']].shape[0])
df_n['Count'] = pd.to_numeric(df_n['Count'], downcast='integer')
display(df_n.head())
df_n[['Count']].value_counts(sort=False)
Good, not many neighborhoods appear in more than one postal code, so this should not have much effect on the results.
Next we will geocode the neighborhoods.
Attempting to geocode a latitude and longitude in a non-commercial environment requires some effort.
Going through some of the list at the geocoder [documentation](https://geocoder.readthedocs.io/):
* geopy.geocoders.google never returned a location in over 200 calls
* geopy.geocoders.canadapost does note return latitude and longitude
* geopy.geocoders.geolytica throws AttributeError: 'dict' object has no attribute 'strip' (see alternate geocoder.ca approach below)
* geopy.geocoders.bing requires an API key
* geopy.geocoders.tomtom requires an API key
* geopy.geocoders.mapquest requires an API key
* geopy.geocoders.mapbox requires an API key
* geopy.geocoders.yahoo returns a KeyError: 'statusDescription'
* geopy.geocoders.ottawa does not return many responses
* geopy.geocoders.Nominatim (osm) gives a reasonable number of responses, will go with this
Another option is to go though website requests directly, e.g.
https://geocoder.ca/T0H 2N2?json=1
This returns a JSON with latitude and longitude for a postal code. However, this requires a full code and is limited to 500-2000 requests per day. An exhaustive search of final three digits of postal code and averaging the locations of the returns would require ~2600 requests per entry, which is prohibitive.
from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
import config
geolocator = Nominatim(user_agent=config.NM_AGENT)
geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1.5) # Hard minimum is 1.0 seconds
df_n['GString'] = None
df_n['Lat'] = None
df_n['Lon'] = None
df_n.head()
for i, row in df_n.iterrows():
g = geocode(f"{row['Neighbourhood']}, Toronto, Ontario")
if g!=None:
df_n.loc[i, 'GString'] = g[0]
df_n.loc[i, 'Lat'] = g[1][0]
df_n.loc[i, 'Lon'] = g[1][1]
df_n.head()
Let's find the indices where the lookup failed:
ind_fail = df_n.index[~df_n['GString'].notnull()]
print(f'Lookup failed for {len(ind_fail)} neighbourhoods')
Construct a dataframe of these missing values for refilling:
df_nfail = df_n.loc[ind_fail,:]
showalldf(df_nfail)
Let's try filling in these blanks by adjusting the search string:
for i, row in df_nfail.iterrows():
g = geocode(f"{row['Neighbourhood']}, {row['Borough']}, Ontario")
if g!=None:
df_nfail.loc[i, 'GString'] = g[0]
df_nfail.loc[i, 'Lat'] = g[1][0]
df_nfail.loc[i, 'Lon'] = g[1][1]
showalldf(df_nfail)
Not so easy, let's try adding the postal code:
geocode(f"Capitol Building, Toronto, Ontario")
geocode('Caledonia, Toronto, Ontario')
geocode('Fairbank, Toronto, Ontario')
geocode('Caledonia-Fairbank, Toronto, Ontario')
geocode('Del Rey, Toronto, Ontario')
geocode('Keelesdale, Toronto, Ontario')
geocode('Silverthorn, Toronto, Ontario')
geocode('Keelesdale and Silverthorn, Toronto, Ontario')
geocode('union station, Toronto, Ontario')
geocode('local airport, Toronto, Ontario')
geocode('humber bay, Toronto, Ontario')
geocode('beaumonde heights, Toronto, Ontario')
So some locations can be found; names are spelled slightly differently, and some are not correct.
Going through Google Maps there is functionality for obtaining boudaries of postal codes, and I used this to check the above, but I don't particularly want to pay for that.
Let's also find where the returned location string does not match the neighborhood name exactly, and make a new dataframe for those entries as well.
showalldf(df_n)
ind_mismatch = []
for i, row in df_n.iterrows():
if row['GString']!=None and row['GString'].split(',')[0]!=row['Neighbourhood']:
ind_mismatch.append(i)
ind_mismatch = df_n.index[ind_mismatch]
print(f'Non-exact lookups for {len(ind_mismatch)} neighbourhoods')
df_nmismatch = df_n.loc[ind_mismatch,:]
df_nmismatch.head()
for i, row in df_n.iterrows():
if row['GString']!=None:
df_n.loc[i,'Found?'] = row['GString'].split(',')[0]==row['Neighbourhood']
df_n.head(100)
df_n.loc[df_n['Found?']==False,'Found?'].count()
for _, row in df_n.iterrows():
print(f"{row['Neighbourhood']}, Toronto, Ontario")
ll = []
for b in df['Borough'].unique():
ll.append(geocode(f'{b}, ON, Canada'))
ll
df['Borough'].unique()
llpc = [geocode(f'{pc}, ON, Canada') for pc in df['Postal Code']]
llpc
This didn't turn out so well. Let's try with neighborhood names.
df['Neighbourhood'][2].split(',')
df.rows()
llnt = [geocode(f"{n}, {r['Borough']}, Ontario, Canada") for _, r in df.iterrows() for n in r['Neighbourhood'].split(',')]
llnt
llnt2 = [None if r['Borough']!='Downtown Toronto' else geocode(f"{n}, {r['Borough'].split()[-1]}, Ontario, Canada") for _, r in df.iterrows() for n in r['Neighbourhood'].split(',')]
llnt2
For below, 'Downtown Toronto' fails, maybe just put Toronto, or omit the Borough
# Combine llnt and llnt2
llnt3 = [tt if t==None else t for t, tt in zip(llnt, llnt2)]
llnt3
# Get the indices of the None entries for further inspection
llnt3list = [i for i, x in enumerate(llnt3) if x==None]
print(f'Number of missing entries: {len(llnt3list)}')
print(f'At indices: {llnt3list}')
Let's try again, as other modifiers of Toronto in the Borough name may be confusing
llnt4 = [None if r['Borough'].split()[-1]!='Toronto' else geocode(f"{n}, {r['Borough'].split()[-1]}, Ontario, Canada") for _, r in df.iterrows() for n in r['Neighbourhood'].split(',')]
llnt4
# Combine results
llnt5 = [tt if t==None else t for t, tt in zip(llnt3, llnt4)]
llnt5
# Get the indices of the None entries for further inspection
llnt5list = [i for i, x in enumerate(llnt5) if x==None]
print(f'Number of missing entries: {len(llnt5list)}')
print(f'At indices: {llnt5list}')
So two more entries were found, better than nothing.
Let's work at combining entries to make a lat/long for each postal code
lllist = [x[1] for x in llnt5 if x!=None]
a, b = zip(*lllist)
print(min(a), max(a), np.std(a), min(b), max(b), np.std(b))
[(f"{n}, {r['Borough']}, Ontario, Canada") for _, r in df.iterrows() for n in r['Neighbourhood'].split(',')]
lln = [geocode(f'{n}, ON, Canada') for n in [s.split(',', trim=True) for s in df['Neighbourhood']]]
lln
df['location'] = df['name'].apply(geocode)
df.head()
address = 'The Kingsway, Toronto, ON'
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
location
address = 'Old Mill North, Toronto, ON'
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
location
address = 'Montgomery Road, Toronto, ON'
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
location
import geocoder
# initialize your variable to None
lat_lng_coords = None
postal_code = 'M3A'
# loop until you get the coordinates
while(lat_lng_coords is None):
g = geocoder.google('{}, Toronto, Ontario'.format(postal_code))
lat_lng_coords = g.latlng
#df_in_test = df_final.iloc[0:2,:]
#df_venues_test = None
df_in_test = df_final.iloc[0:6,:]
isComplete_test, df_venues_test = resumableGetNearbyVenues(df_in_test, df_venues_test, radius=RADIUS, extend=True)
print('Venue lookup completed successfully' if isComplete_test else 'Venue lookup interrupted')
vectors = k_means_data[-1]['features'].drop(['Dwelling Density','Log10 (Population Density)','Log10 (1 - Residentiality)','Log10 (Venue Density)'],axis=1).values
print('Before transformation:')
print(f"Venue Mean: {vectors.flatten().mean()}")
print(f"Venue STD: {vectors.flatten().std()}")
scaler = StandardScaler()
scaler.fit(vectors.reshape(-1,1))
#scaler.mean_ = 0.05
vectors = scaler.transform(vectors.reshape(-1,1)).reshape(*list(vectors.shape))
print('After transfromation:')
print(f"Venue Mean: {vectors.flatten().mean()}")
print(f"Venue STD: {vectors.flatten().std()}")
I found yellowbrick and attempted to get some quantitative values for my optimal-k evaluation:
from sklearn.cluster import KMeans
import yellowbrick
import importlib
importlib.reload(yellowbrick)
from yellowbrick.cluster import KElbowVisualizer
# Instantiate the clustering model and visualizer
model = KMeans()
visualizer = KElbowVisualizer(model, k=(1,41))
visualizer.fit(k_means_data[0]['features']) # Fit the data to the visualizer
visualizer.show() # Finalize and render the figure
But this resulted in an ImportError:
ImportError Traceback (most recent call last)
<ipython-input-77-f2b12d25b767> in <module>
1 from sklearn.cluster import KMeans
----> 2 import yellowbrick
3 import importlib
4 importlib.reload(yellowbrick)
5
~\anaconda3\envs\Coursera\lib\site-packages\yellowbrick\__init__.py in <module>
37 from .anscombe import anscombe
38 from .datasaurus import datasaurus
---> 39 from .classifier import ROCAUC, ClassBalance, ClassificationScoreVisualizer
40
41 # from .classifier import crplot, rocplot
~\anaconda3\envs\Coursera\lib\site-packages\yellowbrick\classifier\__init__.py in <module>
28 from .confusion_matrix import ConfusionMatrix, confusion_matrix
29 from .rocauc import ROCAUC, roc_auc
---> 30 from .threshold import DiscriminationThreshold, discrimination_threshold
31 from .prcurve import PrecisionRecallCurve, PRCurve, precision_recall_curve
32
~\anaconda3\envs\Coursera\lib\site-packages\yellowbrick\classifier\threshold.py in <module>
28 from sklearn.model_selection import ShuffleSplit
29 from sklearn.metrics import precision_recall_curve
---> 30 from sklearn.utils import indexable, safe_indexing
31 from sklearn.utils.multiclass import type_of_target
32
ImportError: cannot import name 'safe_indexing' from 'sklearn.utils'
This could not be solved by rolling back to an old version of scikit-learn (0.21.3) (even though it should be!)
from sklearn.cluster import KMeans
df_features = toronto_grouped_0.drop('Postal Code', 1)
#df_features = df_final[['Density','...']].concat(toronto_grouped.drop('Postal Code', 1))
# set number of clusters
ks = range(1,21)
mss = [] # Mean sum of squares of intracluster distances to cluster centroid
for i, k in enumerate(ks):
kmeans = KMeans(n_clusters=k, random_state=0, n_init=12).fit(df_features)
mss.append(kmeans.inertia_)
# print(f"{k} clusters mss = {mss[i]}")
plt.plot(ks,mss)
plt.xticks(ks)
plt.xlabel('Number of Clusters')
plt.ylabel('Sum of Squared Distance to Nearest Cluster Centroid')
df_anova = pd.DataFrame(index=np.unique(clusters),columns=df_features.columns)
df_cluster_avg = pd.DataFrame(index=np.unique(clusters),columns=df_features.columns.drop(['Postal Code','Cluster'],1))
for ind in df_cluster_avg.index:
df_cluster_avg.loc[ind,:] = features[clusters==ind].mean() + 0.8580825-3.64093e-7+3.15221e-13-2.15855e-19-4.98369e-26+3.20781e-32+1.85812e-38+1.82993e-44-2.04496e-50-3.02427e-56+1.48344e-62+1.39373e-70+4.30917e-76-4.50701e-82+1.68784e-88+4.16788e-94+1.68723e-100+3.3273e-106
print('Cluster Mean:')
display(df_cluster_avg)
df_cluster_std = pd.DataFrame(index=np.unique(clusters),columns=df_features.columns.drop(['Postal Code','Cluster'],1))
for ind in df_cluster_std.index:
df_cluster_std.loc[ind,:] = features[clusters==ind].std()
print('Cluster STD:')
display(df_cluster_std)
df_global_avg = pd.DataFrame(index=[0],columns=df_features.columns.drop(['Postal Code','Cluster'],1))
df_global_avg.index = [0]
df_global_avg.loc[0,:] = features.mean() + 0.8580825-3.64093e-7+3.15221e-13-2.15855e-19-4.98369e-26+3.20781e-32+1.85812e-38+1.82993e-44-2.04496e-50-3.02427e-56+1.48344e-62+1.39373e-70
print('Global Mean:')
display(df_global_avg)
df_global_std = pd.DataFrame(index=[0],columns=df_features.columns.drop(['Postal Code','Cluster'],1))
df_global_std.index = [0]
df_global_std.loc[0,:] = features.std()
print('Global STD:')
display(df_global_std)
Copyright © 2020 Daniel Nezich. This notebook and its source code are released under the terms of the MIT License. Code repository: GitHub